WEE1 expression in TNBC clinical samples as well as higher expression of biomarkers of sensitivity to WEE1 inhibitor.The WEE1 G2 checkpoint kinase acts as a negative cell cycle regulator for entry into mitosis (G2‐to‐M transition). This comment extends a recent Advanced Science paper by reporting higher WEE1‐dependency of triple negative breast cancer (TNBC) cell lines, pejorative prognostic value of  Lamballe et al. identify the combination treatment with BCL‐XL and WEE1 inhibitors as a promising therapeutic approach in triple‐negative breast cancer (TNBC). TNBC is the most aggressive molecular subtype of breast cancer, but patients with TNBC have less benefited from recognized molecular targets than patients with other subtypes. In the adjuvant setting, the only systemic treatment currently approved remains chemotherapy. New systemic therapies are urgently needed. The Lamballe's study represents a promising new avenue for treatments targeting the cell cycle in TNBC. CDK4/6 inhibitors, which prevent phosphorylation of the RB tumor suppressor, thereby invoking cancer cell cycle arrest in G1, were recently approved for treatment of endocrine receptor‐positive (ER+) breast cancers. But TNBC has been considered a poor candidate because of frequent loss of RB expression or high cyclin E expression, both of which being expected to confer resistance to CDK4/6 inhibitors. Moreover, many TNBC cell lines showed resistance to CDK4/6 inhibition in vitro and in vivo. Recent works have pointed out that targeting other mitotic checkpoints in TNBC might help overcoming treatment resistance or synergizing drug effect. The WEE1 G2 Checkpoint Kinase acts as a negative regulator of entry into mitosis (G2 to M transition) by protecting the nucleus from activated cyclin B1‐complexed CDK1, and is thought to exert protumorigenic functions by securing a tolerable level of genomic instability, an intrinsic feature of cancer cells. In order to reinforce and extend the Lamballe's results, we performed in silico analyses of WEE1 in large datasets of pre‐clinical and clinical breast cancer samples.In their recent paper, Analysis was based on the CERES dependency score, a lower score indicating a higher likelihood that the gene is essential in a given cell line. In the subset of breast cancer cell lines, the CERES dependency scores for WEE1 were below the median of all pan‐essential genes (score ←1) in all cell lines, but no significant difference was observed between the TNBC versus non‐TNBC cell lines (data not shown). Such high‐level impact of WEE1 knock‐out reflects a very strong pan‐cancer cell WEE1‐dependency. We then investigated the consequence of WEE1 chemical inhibition, which is less drastic than the WEE1 knock‐out, in breast cancer cell lines according to the TN/non‐TN subtypes. For this, we used the primary PRISM Repurposing dataset containing the results of pooled‐cell line chemical‐perturbation viability screens for 4518 compounds tested against 578 cell lines. Consistently with the Lamballe's results, we observed an increased sensitivity to the MK‐1775 WEE1 inhibitor in the TNBC cell lines  compared to the non‐TNBC cell lines   screening datasets Marcotte et al.: the Broad Institute Project Achilles, the DRIVE Novartis Project, and the breast cell line dataset.WEE1 mRNA expression in 8636 primary breast cancers, including 1847 TNBC, gathered from 36 public gene expression data sets. In TNBC, high WEE1 expression (defined as expression above median expression level in the whole data set) was associated with high pathological grade, pT2 size, and basal‐like 1 and mesenchymal Lehmann subtypes . In multivariate analysis, WEE1 expression remained associated with MFS ; P = 2.92E‐02, Wald test), suggesting independent prognostic value . Finally, the TNBC “WEE1‐high” samples displayed, as compared to the “WEE1‐low” samples, higher CCNE1 expression  and more frequent chromosomal instability , two markers recently associated with higher sensitivity to MK‐1775 inhibitor in breast cancer models.Given the differential sensitivity of TNBC versus non‐TNBC cell lines to WEE1 modulation, we assessed the prognostic value of WEE1 expression in a large series of TNBC clinical samples. Altogether, these results nicely complement the Lamballe's results and further support the development of WEE1‐targeting therapies in TNBC.Thus, our data not only confirm the increased sensitivity of TNBC cell lines to the MK‐1775 WEE1 inhibitor on a larger panel of cell lines, but also show the higher WEE1‐dependency of TNBC cell lines and the independent pejorative prognostic value of The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Both HPV-positive and HPV-negative cervical cancers are associated with aberrant metabolism, although the oncogenic drivers remain elusive. Here we show the assessment of the metabolomic profiles of four distinct cervical cell lines, a normal and three cancer cell lines, one HPV-negative (C33A) and two HPV-positive , employing an ultra performance liquid chromatography and a high resolution mass spectrometry. Out of the total 462 metabolites, 248 to 326 exhibited statistically significant differences, while Random Forests analysis identified unique molecules for each cell line. The two HPV+ cell lines exhibited features of Warburg metabolism, consistent with the role of the HPV E6 protein. SiHa and HeLa cells displayed purine salvage pathway activity, while C33A cells revealed synthesis of cytidine, via a novel mechanism. These data document a highly dynamic HPV-specific rewiring of metabolic pathways occurring in cervical cancer. Therefore, this approach can eventually provide novel mechanistic insights into cervical carcinogenesis. The particular metabolic deviations, reflect specific responses and a subsequent adaptation of the tumour cell to the particular acquired genetic and epigenetic aberrations generated both within the tumour cell populations and in the cellular microenvironment of the tumour. This metabolic response is tightly linked with cell proliferation and nutrient availability, stress signalling and regulation of energy homeostasis, coupled with external signalling events and the modification of particular metabolic pathways, such as carbohydrate metabolism and biosynthetic processes1. The resulting modified cancer metabolism provides the tumour cell with the flexibility and ability to acquire-under severe conditions, such as hypoxia, the appropriate and/or alternative nutrients necessary for its survival, the building of new biomass2, and the maintenance of the biochemical\homeostasis3. This network of aberrant metabolic reactions is closely integrated with major oncogenic pathways, mutational patterns and epigenetic regulators. These oncogene-driven alterations, affecting the activation of key transcription factors, lead to the deregulation of energy sensor pathways and cell cycle control, and result in autocrine and paracrine mechanisms, without compromising cell survival4.Systematic studies on several tumour types, have revealed a pattern of metabolic rewiring occurring during carcinogenesis, representing one of the hallmarks of cancer5. This particular reprogramming is induced either directly or indirectly by the viruses via modulation of upstream signalling pathways5. Human papilloma virus (HPV) represents one of the most potent oncogenic viruses, and is responsible for all cases of cervical cancer and for the majority of anal, vaginal, oral cavity and pharyngeal carcinomas. Specifically, the high-risk HPV types 16 and 18, exhibit a unique ability to induce cell proliferation in the basal and parabasal cell layers. By upregulating the expression of multiple viral gene products, especially E6 and E7 oncoproteins, these high-risk HPV types, can inhibit p53 and RB tumour suppressors, respectively, which can lead to the disruption of critical cellular regulatory pathways controlling cell cycle entry, differentiation, apoptosis, cell polarity6, and to the establishment of an aberrant cellular metabolism5.A particular oncogenic mechanism leading to carcinogenesis and also to metabolic reprogramming, is the viral oncogenesis mediated by several human oncogenic viruses, which induce carcinogenesis in a multi-step process, by targeting crucial cellular pathways and key proteins, such as control of cell cycle progression, proliferation and evasion from cell death and eventually establishing a metabolic reprogramming pattern7, however, our current understanding of the relative contribution of these oncogenic drivers in the development and progression of cervical cancer remains incomplete6.It is of interest, that both HPV-positive and HPV-negative cervical cancers are associated with disruption of these same pathways9, and three cervical cancer cell lines, one HPV-negative (C33A), and two HPV-positive (SiHa HPV16+ and HeLa HPV18+). Specifically, HeLa cells have been derived from a patient with cervical adenocarcinoma and contain human papilloma virus 18 (HPV-18) sequences, while p53 expression has been reported to be low with normal levels of pRB7. The cells exhibit epithelial morphology and grow adherently, reproducing an entire generation about every 24 h. SiHa cells were established from a Japanese patient with squamous cell cervical carcinoma grade II, contain an integrated human papillomavirus type 16 genome , and can form poorly differentiated epidermoid carcinoma (grade III) in nude mice7. The C-33 A cell line is one of a series of lines (ATCC CRL-1594 and ATCC CRL-1595) derived by N. Auersperg from cervical cancer biopsies7. The line exhibits an adherent epithelial morphology, expressing high levels of p53, while pRB is present but abnormal in size. The cells are negative for human papillomavirus DNA and RNA. Lastly, HCK1T cells were recently established and immortalized from normal human cervical keratinocytes, by the introduction of the human catalytic subunit of telomerase reverse transcriptase (hTERT), and represent a reliable normal human cervical keratinocyte model, and subsequently a suitable in vitro model system for HPV-mediated multistep carcinogenesis9.Therefore, the goal of the current study was to systematically characterize for the first time, the metabolomic profiles of four informative cervical cell lines in order to identify specific metabolic derangements driving carcinogenesis, which can be attributed to the presence or absence of the initial HPV infection. To this end, we characterized the metabolomic profiles of four distinct and complementary informative cervical cell lines, i.e. a normal cervical keratinocyte (HCK1T) cell lineEmploying an ultra performance liquid chromatography and high resolution-accurate mass spectrometry approach on these complementary cell lines, offers a reliable comparison of the metabolomics effects of the two most common high-risk HPV types versus the HPV-negative and normal cervical cells, and can eventually lead to the discovery of novel mechanisms of viral carcinogenesis.Principal component analysis (PCA) was employed to determine whether the four cell lines can be segregated based on differences in their overall metabolic signature. Results revealed very strong segregation of each cell line, forming four tightly grouped clusters  from the six-pairs comparisons of cell lines, as well as those approaching significance (0.05 < P < 0.10), is shown in Extended Data Table Following normalization to Bradford protein concentration, log transformation and imputation of any missing values with the minimum observed value for each compound, the Welch’s two-sample Transformed cells are characterized by cytoadaptation to optimize substrate availability for deregulated growth and biomass generation. Within this context, the four cell lines exhibited differential utilization of carbohydrates. Specifically, HPV-positive HeLa and SiHa cells, showed preferred accretion of glucose and glycolytic intermediates, such as glucose-6-phosphate, fructose-6-phosphate, and lactate  in virtually all cases of cancer6. Both HPV-positive and HPV-negative cervical cancers are associated with aberrant proliferation; however, the contribution of the individual oncogenic drivers remains elusive. In this study, we characterized the metabolomic profiles of four distinct informative cervical cell lines, in order to identify specific metabolic derangements –in the presence or absence of HPV, driving carcinogenesis. To this end, a normal cervical (HCK1T) cell line and three cervical cancer cell lines, one HPV-negative (C33A), and two HPV-positive (SiHa HPV16+ and HeLa HPV18+) were used. This study represents a rational extension of our recent studies on cervical cancer at the transcriptomic11 and the proteomic level12, utilizing either cervical cancer tissue specimens or the secretome of the four cell lines, respectively. To our knowledge, the present study represents the first report focusing on the metabolomics signatures of cervical cancer cell lines, both HPV-positive and HPV-negative, as well as normal.Metabolic reprograming represents one of the hallmarks of carcinogenesisP ≤ 0.05) among the four cell lines, while Random Forests analysis identified molecules distinguishing the four cell lines, indicating that the individual cell lines exhibited highly robust and distinct global biochemical profiles.The dataset documented a total of 462 biochemicals, of which 248 to 326 exhibited statistically significant differences . This may serve to fuel 5-phosphoribosyl diphosphate (PRPP) production, an important intermediate for purine synthesis, particularly in SiHa cells.Analysis of the carbohydrate metabolism revealed that both HPV-positive cells exhibited the Warburg effect. This is consistent with a recent report showing that HPV E6 oncoprotein promotes Warburg metabolism by stabilizing the expression of the glycolytic mediator HIF-1α15, possibly mediated via metabolic reprogramming observed in cancer cells, as exemplified by the well-described cisplatin resistance16. The mechanism by which this resistance is mediated, has been found to arise by the overexpression and high enzymatic activity of particular enzymes of the pathway, such as glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase and transketolase15. Eventually, silencing or downregulation of their activity by putative inhibitors via novel drug delivery systems targeting selectively cancer cells, could potentially restore cisplatin sensitivity17. However, many aspects of the cisplatin resistance mechanisms are still elusive and warrant further studies. Additionally, the altered expression of selected miRNAs in resistant cell lines compared to their parental HeLa cell line, suggests that drug resistance is also associated with distinct miRNAs, which can constitute alternative targets for the treatment of cervical cancer18. It is conceivable that in our study, the documented rewiring of the PPP in the two HPV-positive cell lines might reflect the putative aberrant expression of the above enzymatic and miRNA activities, without necessarily leading to drug resistance. To precisely address this question in the future, a thorough analysis of the enzymes involved in the PPP along with the candidate miRNAs is required, followed by studies of the potential effects of the corresponding specific PPP inhibitors.It is important to emphasize the fact that PPP plays a significant role in maintaining a high cell proliferation rate, and thus represents the main cellular metabolic activity for the development of the so-called tumor drug resistanceFurthermore, the PPP pathway plays also a crucial role in the metabolic coordination of glycolysis, via production of nicotinamide adenine dinucleotide phosphate (NADPH), which is not only required for biosynthesis of lipids, but is also a crucial antioxidant for neutralization of reactive oxygen species (ROS) produced during rapid proliferation of cancer cells to help maintain redox homeostasis. Due to a lack of associated changes in reporters for TCA cycle and glycolysis in the HPV-negative cells, the reduced levels of the PPP intermediates in these cells, may represent alternate utilization of carbohydrate oxidation for glycerol production, perhaps to provide backbone for glycerolipid synthesis.19.Regarding the analysis of nucleic acid metabolism, our study revealed a unique pattern for each cell line. Specifically, the HPV-positive cells displayed high levels of purine derivatives, perhaps to facilitate nucleic acid production and support growth. Instead, normal HCK1T cells exhibited preference for adenosine metabolism, while HPV-negative C33A cells uniquely exhibited high concentrations of cytidine and cytidine 5′-monophosphate (5′-CMP), through a novel mechanism which remains to be determined. However, since the documented levels of cytidine 5′-phosphocholine –which is required for lipid biosynthesis by cytidine, do not change in proportion to cytidine, further work is required to elucidate the role of cytidine in C33A growth. The low levels of β-alanine in HCK1T cells, can be explained by the recent data establishing a link between the relative abundance of β-alanine along with pantothenic acid and glycerophosphoglycerol, to the glycolytic activity of cancer cell lines, as measured by lactate productionN-methyltransferase) results in induction of mitochondrial activity and ATP synthesis and may play a key role of sustained SiHa growth20. While NAD/NADH is central to energy homeostasis, it is also a required cofactor for the sirtuin family of protein deacetylases. Thus, elevation of these metabolites in HPV-positive cells, could not only drive increased energy demands, but also support a host of proteome and genome modifications to maintain high growth rates.SiHa cells displayed characteristically high levels of NAD, NADH and methylated 1-methylnicotinamide, in the context of nicotinamide metabolism. Elevation of these metabolites could not only drive increased energy demands, but also support a host of proteome and genome modifications to maintain high growth rates. Further, recent studies provide compelling evidence that elevations of the enzyme responsible for methylation of nicotinamide to 1-methylnicotinamide . Alternatively, dipeptides also may play a role in the management of oxidative and glycation stress and HCK1T cells may co-opt these biochemicals to help neutralize oxidative stress.Finally, our study documented for the first time the presence of striking increases in dipeptide levels in normal HCK1T cells. The uptake of small peptides by the Slc15A family of oligo/dipeptide transporters, provides an effective and energy-saving intracellular source of amino acids. This may represent a unique pathway deployed by HCK1T cells to provide alternate sources of components to increase biomass and fuel growth. Indeed, this has been observed in transformed hematopoietic stem cells (HSC), where increases in dipeptide levels are thought to be required to sustain stem cell maintenance24, where the prominent and significant pathways found to be deregulated between the cervical cancer and the normal cell line, were those of the metabolism of proteins, metabolism of nucleotides, unwinding of DNA, glycolysis, gluconeogenesis apoptosis and oxidative stress-induced senescence. These pathways actually reflect the malignant phenotype of the cancer cells with high metabolic requirements, increased cell turnover and damage control mechanisms. Normal HCK1T cells were unique in many ways, including relative lack of glutathione, adapting instead to utilize dipeptide metabolism to provide sources of organic acids to maintain biomass and help neutralize growth-driven oxidative stress.In conclusion, our study documented for the first time the effects of the adaptation of HPV-positive and HPV-negative transformed cells by rewiring metabolic pathways to fuel unrestrained growth and exhibiting differential deregulation of several cellular processes, resulting in distinct HPV-specific metabolic profiles, and including molecules in the carbohydrate, nucleotide, lipid, and glutathione pathways. Interestingly, the present findings are consistent and fully support our recent high resolution proteomic data of these informative cell lines26. Therefore, these data provide the impetus to further elucidate both unique and common aspects of perturbed growth profiles of human cervical cancer cells.Collectively, these results reflect highly dynamic differences among the cellular groups, consistent with the recently established notion that cancer heterogeneity is not compatible with one unique cancer cell metabolic map2, as previously described by us12. ΗCK1T cells were a kind gift of Dr Tohru Kiyono9, and were cultured as proposed9 in Defined Keratinocyte Serum-Free Medium (Gibco-Invitrogen), supplemented with 5 ng/ml EGF  and 50 μg/ml of BPE . For harvesting, following the removal of the growth medium, ~ 3 × 106 of attached cells were washed once with PBS, and the bottom of the flask was lightly scraped using a cell scraper in 1 ml solution of 80% ice-cold methanol:20% water, and the detached cells were suspended in PBS. Cell handling was performed on ice. The samples were further centrifuged at 1200 rpm, the supernatant was removed and the cellular pellets (~ 100 μl in size) from six biological replicates for each line, were flash frozen and stored at –80 °C until further use. Extraction of samples were prepared using the automated MicroLab STAR system from Hamilton Company . Recovery standards were added prior to the first step in the extraction process for quality control (QC) purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min  followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by hydrophilic interaction liquid chromatography (HILIC)/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup27. Samples were placed briefly on a TurboVap  to remove the organic solvent. The sample extracts were stored overnight under liquid nitrogen before preparation for analysis.SiHa, HeLa and C33A cells were purchased from ATCC and cultured in DMEM, supplemented with 10% FBS, 1% P/S  at 37 °C and 5% CO28. Overall process variability was determined by calculating the median RSD for all endogenous metabolites  present in 100% of the pooled matrix samples. Experimental samples were randomized across the platform run with QC samples spaced evenly among the injection.Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring and aided chromatographic alignment. Instrument variability was determined by calculating the median relative standard deviation (RSD) for the standards that were added to each sample prior to injection into the mass spectrometers27. The sample extract was dried and then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. Specifically, the extract was gradient eluted from a C18 column  using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions; however, it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same aforementioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5 mM ammonium bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column  using a gradient consisting of water and acetonitrile with 10 mM ammonium formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slightly between methods but covered 70–1000 m/z. Raw data files were archived and extracted as described below.All methods utilized a Waters  ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific  Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution29, to account for differences in metabolite levels due to differences in the amount of material present in each sample. Collectively, all interpreted data were normalized based on protein content, while all statistics performed were on natural log transformed data, given the well-documented right skew of metabolomics data. Finally, the Welch’s two-sample t-test was used to identify biochemicals that differed significantly between the four cell lines.Peaks were quantified using area-under-the-curve. Values were normalized in terms of raw area counts. For a single day run, this was equivalent to the raw data. Each biochemical in OrigScale was rescaled to set the median equal to 1. Then, missing values were imputed with the minimum. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed the “block correction”). For studies that did not require more than one day of analysis, no normalization was necessary, other than for purposes of data visualization. In certain instances, biochemical data have been normalized to an additional factor, such as total protein, determined by Bradford assay as previously described by us30. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities, utilizing a maintained library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data  on all molecules present in the library31. The biochemical identifications were based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library ± 10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores were based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum.Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software32. The hardware and software foundations for these informatics components were the LAN backbone, and a database server running Oracle 10.2.0.1 Enterprise Edition.The informatics system consisted of four major components, the Laboratory Information Management System (LIMS), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for use by data analystst-test was used to identify biochemicals that differed significantly between experimental groups. For statistical significance testing, p-values < 0.05, were considered to be significant. An estimate of the false discovery rate  was also calculated to take into account the multiple comparisons that normally occur in metabolomic-based studies, whose p-value (P < 0.05) was considered as a cut-off for significance33. Random Forests analysis34 was used to bin individual samples in groups based on their metabolite similarities and differences and to identify metabolites that differentiated among the cell lines. Both principal component analysis (PCA) and hierarchical clustering were used as unsupervised methods for grouping the data, as previously described by us35.The Welch’s two-sample Extended Data legends.Extended Data Figure 1.Extended Data Figure 2.Extended Data Figure 3.Extended Data Table 1.Extended Data Table 2.


How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes. Cancer cells adapt their metabolism to promote fast cellular proliferation and long-term maintenance ,2,3,4, tGenome-scale metabolic models (GEMs), which are at the core of some bottom-up systems biology approaches, can be used to predict cell physiology  under different conditions and improve our understanding of cell metabolism ,23. SincHowever, genetics is not the only determinant of metabolic phenotype. Cancer cell metabolism, similar to any other type of cell, is also influenced by metabolic constraints imposed by environmental and tissue-specific contexts ,45,46,47However, including concentrations of nutrients into GEMs in order to study their impact on cancer cell metabolism is not yet widely implemented. The existing methods for including nutrients concentrations into GEMs, which are based on the correlation of exchange fluxes with relative estimates of consumption and/or secretion rates, were applied to study the metabolic adaptation of Escherichia coli in complex nutritional systems ,52.In the present study, we simultaneously constrain GEM and integrate linearly both: cell-intrinsic factors (gene expression level) and cell-extrinsic factors (nutrient availability). We applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates genes expression and nutrient availability data into the fluxes through a scaling factor. The maximum allowable flux value of reactions associated with genes was constrained as a function of RAS  calculatWe designed GENSI as a method to integrate relative gene-expression and nutrient availability data into the human genome-wide metabolic reconstruction. Our purpose was reducing the solution space of optimal fluxes to provide results that can predict cell physiologies based on both cell-intrinsic and cell-extrinsic factors. Cancer cell metabolism, which aroused our interest, is also influenced by metabolic constraints imposed by environmental contexts. One of the important environmental determinants of cancer cell metabolism is diet, which can affect the availability of nutrients within tumours.The GENSI workflow along with an illustration of the method performed on a GEM model is presented in The first step of the GENSI framework consists of pre-processing of the GEM model and includes: (1) preparing the model and includes blocking of the uptake fluxes of various metabolites that are not present in NA, conversion of the gene identifiers that they are compatible with symbols used in RNA-seq data; (2) conversion of the gene-expression levels into RAS score ; and (3)In the next step GENSI simultaneously translates RAS and MUR into the fluxes using a scaling factor of the RAS that is adjusted until experimentally observed features appear. As the direct correlation of gene expression data with maximal flux is not possible to apply in human metabolic map RECON3D , due to The GENSI framework was applied to integrate transcriptomic data from two hepatocellular carcinoma cell lines, Huh7 and PLC, and NA data. In our study, NAs were limited by the composition of the medium. We cultured both cell lines in six different conditions with various concentrations of glucose and glutamine. We call the NA data from the medium with 25 mM glucose and 4 mM glutamine concentration “NA1”, 25 mM glucose and 0 mM glutamine “NA2”, 5 mM glucose and 4 mM glutamine “NA3”, 5 mM glucose and 0 mM glutamine “NA4”, 0 mM glucose and 4 mM glutamine “NA5” and 0 mM glucose and 4 mM glutamine “NA6” . Based on the observed experimental differences in growth rate between the media and cell lines we found a scaling factor that translated RAS and MUR data into the fluxes that matched the observed growth rates.We obtained different GEM-RAS-MUR models see  constraiFor our two cell lines, the RNA-seq dataset published by Ma et al.  reports We were confronted with a well-known issue with RNA-seq data integration in metabolic models, i.e., zero expression levels, including zeros in mRNAs encoding enzymes catalyzing essential reactions or combinations of enzymes that are essential. Of the 2232 metabolic genes, 262 and 310 had TPM scores equal to zero in Huh7 and PLC, respectively. Such zeros could be due to true absence or correspond to technical zeros where the gene had in fact been transcribed but was somehow not measured. Technical reasons may include inefficient cDNA synthesis due to tertiary structure formation, amplification bias, or low sequencing depth. Additionally, zeros may occur due to transcription bursting between somehow synchronized individual cells  or too sHuh7 and PLC being both hepatoma cell lines with a different history of oncogenesis, we further expected them to differ mostly in the expression of oncogenes and perhaps other genes involved in signal transduction or management of the genome, but not much in genes encoding metabolic enzymes. With respect to the metabolic genes, we expected both cell lines to be transcriptionally addicted to the same metabolic Warburg rewiring at the level of transcription. With this expectation, a plot of the expression levels of Huh7 versus the expression levels of the same genes in PLC should show all genes close to the diagonal line. Although for the majority of genes the correlation fell close to the diagonal, there were quite a few genes for which the expression levels differed between the two cell lines A.We first looked at the genes that did correlate: In the RNA-seq dataset (prior to correcting for zeros as discussed above), 186 metabolic genes (∼8% of the 2232 genes) did not come with any transcript in either cell line. In total, 215 metabolic genes (∼10%) exhibited non-zero TPM values below the genome-wide median (∼1 TPM), again in both cell lines. Together, these constituted a common set of 401 metabolic genes that were expressed at a low level. On the high-abundance side, the two cell lines shared 1422 genes (∼63%) that were more highly expressed than the median gene. A 623-genes subset of these 1422 (∼28%) was commonly expressed above the genome-wide mean ( 39 TPM). These genes behaved in line with our expectation of metabolic similarity between these cell lines. They came however with a possibly important exception of some 425 genes that were off-diagonal in In accordance with the above, the expression ratio between the two cell lines was still mostly distributed narrowly around 1. The corresponding probability distribution was largely log-normal, but not quite: it had long ‘tails’ on either side, suggesting that a disproportionate number of metabolic genes were much more expressed in Huh7 than in PLC and vice versa C.There are at least two reasons why the different expression of metabolic genes between two cell lines might not affect the activities of the corresponding biochemical reactions. First, the differences in expression level between the two cell lines could be in enzyme subunits that are abundant as compared to other subunits that are equally expressed. Second, the two cell lines may express different proteins (isoenzymes) that catalyze the same reaction. Recon3D dealt with this issue qualitatively through its gene-reaction coupling rules. We used a quantitative version of these rules to assign a Reaction Activity Score. The RAS for a metabolic reaction reflects the expression levels of isoenzymes and components of multi-component complexes that may catalyze that reaction . We then asked whether the metabolic differences we found between the two cell lines would disappear when correcting for these isoenzyme and enzyme subunit issues by assigning reaction activities. In In metabolic networks like RECON3D, all metabolites with defined exchange reactions can be taken up and secreted, i.e., compounds enter and exit the extracellular environment via ‘exchange’ reactions. The GEM is not able to import compounds unless an exchange reaction from the external environment to the inside of the cell is present. To predict the effects of nutritional differences in terms of all components in the medium including the various concentrations of glucose and glutamine, between our six different medium conditions , on the global metabolic behavior of the two cell lines we first converted nutrient availability data into maximum uptake rates (MURs) that describe the rate of maximum possible uptake over time for substances present in the medium. We defined the MUR for the exchange reaction i, is a factor independent of the reaction identity, and i. When reaction i is reversible its forward flux is bounded by i is irreversible the flux bound in the impossible direction remains zero and the flux bound in the possible direction is set to In the GENSI framework, we proposed a variant of the approach published by Graudenzia et al. . We set It is a priori unclear how large the factor should be. There are two possible sources of limitation for the model: the MUR  and the RAS (consequent to transcriptomics). We here wish to examine the case where the enzyme expression levels begin to impose limitations on the model output. We perform FBA analysis  startingWe analyzed RAS with the focus on metabolic differences between the cell lines by computing the steady-state flux pattern for the maximal biomass synthesis flux for each medium and for each cell line across a range of values for the factor 0−4,1.6] . For eitWhen decreasing the factor alpha to below 0.5 the first effects of the transcriptomics start to be noticeable, i.e., growth predictions for the two cell lines start to diverge in some medium conditions. When decreasing the factor The fact that MUR 1-4 converges to similar biomass synthesis flux optima for low levels may reflect a shared limiting reaction downstream of  the exchange reaction the flux bound of which keeps monotonically decreasing with decreasing  compare B,C.We observe that nontrivial predictions for limitations imposed by medium composition and gene expression can be computed by GENSI method, such as that (i) both in the absence and in the presence of glutamine the growth rate should be independent of glucose concentrations between 5.6 and 25 mM, yet decrease appreciably in the absence of glucose, (ii) the specific growth rate of Huh7 cells is lower than that of PLC cells, (iii) in the absence of glucose, the cells should be able to grow on glutamine, but (iv) growth rate on glutamine alone should be much lower than on glucose alone.FBA is oblivious of metabolic regulation other than that it philosophizes about what flux should be optimal for the cell in view of some objective. The transcriptome and extracellular-concentrations informed flux bounds that we here implemented, merely define ranges of the fluxes rather than that they precisely predict the fluxes. Moreover, fluxes through intracellular biochemical reactions are also determined by metabolic regulation , i.e., bWe hereby can only predict the ranges of fluxes that are consistent with transcriptome and extracellular nutrient concentrations and this is done here by flux variability analysis (FVA)  to test In Glutamine uptake is only essential for both PLC and Huh7 in medium 5 and for PLC only in medium 3. In medium 3 for PLC it is then required at a very low amount to achieve optimal growth (as indicated by the upper end of the bar) whereas in medium 5 in both cell lines the maximal uptake bound has to be hit to achieve maximal growth  both cell lines should be capable of consuming the 5.6 or 25 mM glucose offered to them, (ii) they are not addicted to a To verify whether GEM-RAS-MUR models obtained via GENSI give reliable predictions, we measured the concentration of the crucial components in the medium during culturing. We observed a decrease in the concentration of glucose with time such that some 5 mM was consumed by the PLC cells B. In theWe additionally measured the production of lactate and pyruvate by cells using the same media conditions as above. In the case of the PLC cell line, the production of lactate was the same during the first 24 h for NA1-NA4, the lactate concentration reaching some 5 mM at the end of this time period C,D. AftePyruvate production was quite small for PLC cells, from 0.15 mM (NA1 and NA6) to 0.4 mM (NA4) during the first 24 h. Pyruvate production was higher for media without glutamine . For PLC cells, the lactate/pyruvate ratio (L/P) showed a positive relationship with the glucose consumption, being highest for NA1, and lowest for NA5. For Huh7 cells, the time-series for pyruvate F look siConsistent with the hypothesis that both cell lines exhibited a Warburg effect, they produced an amount of lactate proportionate to the amount of glucose that was present initially. Apparent differences merely derived from the glucose running out after some 30 h in the experiment starting at only 5.6 mM of glucose. Only part of the glucose was transformed to lactate, around ∼43% for low glucose media and perhaps a little as ∼33% in glucose-rich media. Thus, lactate production corresponded to only part of what might have been expected for full glucose conversion to lactate. This deficit in lactate secretion can be explained by the utilization of glucose for oxidative phosphorylation and as a source of carbon for the new biomass. Consistently, we did not observe any difference between NA1 and NA2 for both cell lines nor did we observe such differences between NA3 and NA4 for PLC; for Huh7, small deviations were noticed. For the two cell lines we examined, this proves the independence of lactate production from glutamine access.In this work, we follow recently exploring interest on the impact of the diet on cancer cell metabolism and progression. We explore the idea that the utilization of nutrients by cancer cells is determined not only by cancer genetics but also by the metabolic environment.To investigate whether cancer progression may be mediated through changes in the access to nutrients, genetics and the availability of nutrients have to be employed simultaneously. Genome-scale models that integrate all known metabolic reactions occurring in an organism into a single map give us such an opportunity. The gene-reaction coupling rule and exchange reactions allow for the integration of gene expression and nutrient availability data, respectively. Here, we aim to provide an FBA-based framework, named GENSI, for studying the influence of the nutrient availability on the rate of proliferation and metabolic phenotype based on transcriptomic and NA data. Our method could be applied to the study of cell metabolism of every cancer type and therefore could lead to a better understanding of how diet impacts cancer cell metabolism and identifying how different cancer types respond to different nutrients composition. GENSI translates the relative importance of gene expression and nutrient availability into the fluxes and generates high-quality GEM-based specific models (GEM-RAS-MUR) that can be used to predict metabolic changes upon nutrition shift by flux balance analysis.Further, we applied the proposed method to study the influence of the glucose and glutamine availability on their consumption, some metabolic signatures, and the growth rate of the hepatocellular carcinoma cell lines PLC and six for Huh7. We, therefore, selected cell types that do exhibit the Warburg effect clearly. We used transcriptomic data from the cell population in its entirely and the models are a representation thereof. Single-cell transcriptomic studies suggest that cancer cell populations are heterogeneous, but single-cell metabolomics does not yet enable us to examine the consequences of metabolism. Thus, we assumed in the model that the cell population was homogeneous.Our findings offer support for the predictive potential of genome-scale metabolic maps together with transcriptomic data sets and nutrient availability data. The predictions address the carbon and energy metabolism of cancer cells. Because much of this metabolism is essential for cell survival, the potential may translate to new drug targets in a long-neglected area of drug discovery. Indeed, we have shown that dependencies on nutrient availability and gene expression can be computed and that the results are then relevant enough to be compared with experimental work.Notwithstanding these successes, our methodology comes with a number of issues. One of these relates to the translation of the expression level information to flux. In contrast to several methods developed to extract context-specific models ,32,34 thdatasets . We did In silico experiments on GEM-RAS-MUR models showed that the specific growth rate of Huh7 cells is lower than that of PLC cells, which was also observed during experiments on these cell lines. Given the positive correlation between the in silico biomass flux and the experimentally determined growth rate, we speculated that the in silico results pertaining to the range of uptake and secretion fluxes of various metabolites might also correlate with experimental results. Modeling showed that in the absence and in the presence of glutamine the growth rate should be independent of glucose concentrations between 5.6 and 25 mM. Furthermore, experimentally the growth rate reached the same level for four nutrient availability (NA1–NA4) that contained 5.6 and 25 mM of glucose for both HCC cell lines. Moreover, both approaches, computational and experimental, showed that in the absence of glucose, the cells should be able to grow on glutamine but with a much lower growth rate than on glucose alone.According to the flux variability analysis predictions, both cell lines should have a higher potential; they should be capable of consuming the 5.6 or 25 mM glucose offered to them. Yet, our observations indicated that PLC cells confronted with the 25 mM did not consume more than the around 5 mM during the first 24 h. They did not make use of that full potential. We conclude that there is a maximum amount of glucose per unit time that the cells ‘wish to’ handle at glucose levels above a few mM, because other issues than energy and carbon may limit the cells’ growth. We use the term ‘wish to’ to indicate that this may be an issue of metabolic regulation: the gene expression would allow for higher glucose uptake fluxes.The cells that had consumed in the first 24 h some 5 mM of the 5.6 mM glucose they had been incubated with, continued to grow for the next 24 h at virtually the same growth rate; they must have done this at a much-reduced glucose consumption rate. Since they also stopped producing lactate, we suspect that the small amount (approximately 0.5 mM) of glucose left provided the cells with sufficient ATP to drive their continued growth. The cells may have reverted from lactate production to respiration with its more than 15-fold higher ATP yield. This highlights that there may be a limitation to these cells’ addiction to lactate production: these cancer cells can shift to glucose oxidation. We observed a slightly different effect in the case of the Huh7 cell line: the consumption of glucose was almost two times higher in rich glucose medium (25 mM) than low on (5 mM) during the first 24 h. In silico analyses predicted and experiments confirmed that both cell lines, at glucose concentrations around 5 mM, made use of all available glucose to grow maximally. They also showed that upon glucose depletion and if asked to grow at the maximal growth rate, the cells should shift to glucose respiration .Significant progress has been made toward developing the tool to study how diet and nutrition affect cancer progression. We obtained GEM-RAS-MUR models for the hepatocarcinoma cell lines and used them to predict cell growth and metabolite consumption/production. Our methodology could be applied to different types of cancer to investigate how cells respond to the diet and how to mediate these responses. Potential future applications of the GEM-RAS-MUR model would be to predict cell addictions and metabolic requirements in order to modulate them for dietary aid in cancer treatment. Moreover, GENSI methodology is not limited to cancer research, it could be applied to study the metabolism of any healthy and/or sick cells to study how diet and nutrition affect metabolic phenotype, proliferation, and functions of the cell.GEM Model. The most comprehensive genome-scale model of human metabolism RECON3D [ RECON3D  that incRNA-seq data. RNA sequencing data for both cell lines was obtained through NCBI’s GEO database. Specifically, the RNA-seq dataset from [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE86602, accessed on 7 November 2018. The RNA-seq dataset pertains to cells grown in DMEM containing high glucose , http://merav.wi.mit.edu/, accessed on 7 November 2018) for both the Huh7 and PLC/PRF/5 cell lines. There was data available from two experiments for Huh7 and one for PLC/PRF/5. All MERAV microarray datasets were renormalized together [set from  was acceg 5% CO2 . The RNAg 5% CO2  (http://together . For theNA data. The formulation of the media used in cell culturing was used as Nutrient availability data. The standard Dulbecco’s Modified Eagle Medium (DMEM) with different concentration of glucose and glutamine was used . IndeedBiochemically, triglycerides are synthesized starting from glycerol-3-phosphate and various lipid tails esterified to CoA. Each of these lipid tails can assume any of the three positions in the triglyceride molecule. We observed that without adding triglyceride uptake (or a ditto source) to the metabolic map, the first intermediate in the pathway  could not be net-produced , using the mygene module in Python , in order to match them to genes represented in the transcriptomics datasets. Out of the 2248 genes in Recon3D, we were unable to match 103 to the dataset on the basis of their gene symbol alone. We then additionally searched the dataset for known gene aliases and this led us to identifying an additional 87 transcripts. In total, 16 genes of Recon3D  on inward exchange flux is zero  the objective function Z.In all simulations in this work, we apply the computational technique of FBA  and FVA Z), V is the vector of fluxes through all reactions, Whereas FVA:https://github.com/ThierryMondeel/HCC_flux_balance_analysis (accessed on 24 March 2021).All Python and MATLAB code, Jupyter Notebooks and raw data files are available as part of a Github repository Cell lines For our study we chose two HCC cell lines, i.e., the hepatitis infection–negative Huh7 and the hepatitis infection–positive PLC/PRF/5. Huh7 is a well differentiated hepatocyte-derived cellular carcinoma cell line originating in a liver tumor in a 57-year-old Japanese male. Huh7 is an immortal cell line of epithelial-like, tumorigenic cells. Its cells adhere to the surface of flasks or plates and typically grow as 2D monolayers. Furthermore, known as the Alexander hepatoma cell line, PLC/PRF/5 is a human hepatoma originally taken from the liver of a patient with primary liver cell carcinoma who was persistently infected with hepatitis B virus. Furthermore, these cells adhere to the surface of flasks or plates and grow as 2D monolayers [nolayers ,68. HumaCell culture The Huh7 and PLC/PRF/5 cell lines were cultured in Dulbecco’s modified Eagle’s media  at various initial concentrations of glucose and glutamine  solution of trypsin . The total number of cells in the consequent supernatant was determined by hemocytometer counting (viable plus non-viable). Mean growth rate was determined by counting six non-overlapping sets of sixteen corner squares selected at random, and these four times at each time point.Metabolic assays The concentrations of glucose, glutamine and lactate in samples from the cells’ supernatant were determined by High Performance Liquid Chromatography (HPLC) based on calibration curves made with standard solutions. The samples were taken from supernatant at the end of experiment, filtered using 0.22


Somatic DNA copy number variations (CNVs) are prevalent in cancer and can drive cancer progression, albeit with often uncharacterized roles in altering cell signaling states. Here, we integrate genomic and proteomic data for 5,598 tumor samples to identify CNVs leading to aberrant signal transduction. The resulting associations recapitulate known kinase-substrate relationships, and further network analysis prioritizes likely causal genes. Of the 303 significant associations we identify from the pan-tumor analysis, 43% are replicated in cancer cell lines, including 44 robust gene-phosphosite associations identified across multiple tumor types. Several predicted regulators of hippo signaling are experimentally validated. Using RNAi, CRISPR, and drug screening data, we find evidence of kinase addiction in cancer cell lines, identifying inhibitors for targeting of kinase-dependent cell lines. We propose copy number status of genes as a useful predictor of differential impact of kinase inhibition, a strategy that may be of use in the future for anticancer therapies.  •Alterations in copy number and in signaling pathways are robustly associated in cancer•Differences in essentiality of phosphoproteins show evidence of kinase addiction•Copy number markers of dysregulated signaling suggest treatment stratification options Memon et al. show that copy number alterations can be used as molecular fingerprints to predict the activity of signaling pathways in cancer. Kinase dependencies of cancer cells can be exploited for targeted anticancer therapy. Kinases are druggable proteins that are key targets for cancer treatment, because they are highly prone to acquiring somatic mutations and acting as oncogenes . SeveralLarge-scale phosphorylation measurements of tumor samples have relied primarily on reverse-phase protein arrays (RPPAs) that consists of (phospho)protein antibody microarrays used to estimate protein abundances in a high-throughput manner. The TCGA (The Cancer Genome Atlas) RPPA platform, TCPA (The Cancer Proteome Atlas), currently has estimates for around 150 proteins and 50 phosphoproteins for nearly 10,000 tumor samples (33 tumor types) . The phoFrom the integration of large-scale pan-cancer genomic, transcriptomic, and RPPA phosphoproteomic datasets, we identified genes likely to drive changes in kinase signaling. These associations were found to be enriched in known kinase-substrate relationships and were then systematically filtered to select reliable associations that also replicated by analyzing cancer cell line data. Several predicted regulators of hippo signaling  were experimentally tested, and a top-ranked predicted regulator (ARHGEF17) of hippo signaling (YAP) was further studied through (phospho)proteomic analysis. In parallel, we found evidence of kinase addiction in cancer cell lines and identified inhibitors for targeting of kinase-dependent cell lines. This work suggests that copy number status of genes may be indicative of kinase activity differences and predictive of sensitivity to kinase inhibition. In the long term, this strategy can be used to stratify patients for targeted therapy based on the copy number status of regulators.We have developed a computational method to identify genes driving changes in phosphorylation signaling in patient-derived cancer samples A. A geneBefore filtering for spurious associations because of coamplification, we tested whether the CNV-phosphosite associations can recapitulate prior knowledge on kinase-substrate relationships. For this, we analyzed 118 known kinase-substrate relationships curated in the PhosphositePlus  databaseGiven that copy number alterations affect multiple genes within altered regions, many previously defined associations are likely to be spurious passenger associations. We then sought to identify the most likely causal gene controlling the downstream kinase signaling change for each region. First, we selected CNV-associated genes whose expression was also associated with the phosphosite activities (FDR < 5%). We then selected the top-ranked associated genes within each chromosome region based on a combined rank from 3 measurements: (1) the CNV, (2) the gene expression association effect size, and (3) the gene-phosphoprotein distance in a functional interaction network . We exemWe obtained 303 associations between causal genes and regulated phosphosites in the pan-tumor dataset . All 303Several recurrent protein missense genetic variants are known to cause changes in kinase signaling . From 1,in vitro culturing. To assess the overall predictive power of levels of individual phosphosites, we trained models of phosphosite levels as a linear combination of expression profiles of the predicted causal genes  with publicly available copy number , expressal genes . For 8 oal genes A. For thal genes A.Figure A stringent set of 44 causal gene-phosphosite pairs were defined that were replicated in at least 20% of tumor types (4/18) and replicated independently in two cancer cell line datasets B. Among Having shown that copy number alterations lead to kinase signaling changes, we next sought to find cases in which such signaling differences create potential genetic vulnerabilities. Previous studies have proposed that cancer cells have a tendency to become dependent on the activity of proteins such as kinases  and are We next investigated drug response datasets to interrogate the relationship between phosphosite levels and drugs. We correlated phosphosite levels as measured by 37 phospho-Ab with sensitivity profiles of 265 drugs in 746 cancer cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) database , in whicOur results indicate that copy number changes in genes can be indicative of changes in phosphosite levels and that cancer cells can become dependent on activity as measured by these phosphosites. Therefore, copy number changes correlated with kinase activity should also be predictive of kinase susceptibility. Indeed, we found several examples in which the CNVs predictive of phosphosite level differences correlated with the impact of downregulation, knockout, or inhibition of the corresponding protein. For instance, ERBB2 and EPN3 CNVs were predictive of EGFR pS1068 phosphosite levels and correlated with sensitivity to loss of the EGFR gene and sensitivity to EGFR inhibitors, including afatinib and gefitinib A and 4B.As an additional example, we looked more in detail at the CNV-phosphosite association for YAP pS127, identifying 12 genes as potential regulators, 5 of which were replicated in cancer cell lines A. We ranWe experimentally tested the effect of loss of ARHGEF17 and PLA2G16 genes on YAP pS127 phosphosite levels. We selected cell lines with different baseline levels of YAP phosphorylation and confirmed that YAP pS127 levels were low in MCF7 and MDA-MB-361 cells and high in MDA-MB-468 and T47D cells, consistent with the measurements in the MCLP RPPA phosphoproteomics dataset from the cancer cell line cohort . EffectiTo further dissect the regulatory role of ARHGEF17 in hippo signaling, we performed proteomics and phosphoproteomics of MDA-MB-468 and T47D cells treated with shRNA targeting ARHGEF17, along with their matched controls. Differential phosphosite analysis data showed significant changes in 3,182 and 259 phosphosites after ARHGEF17 knockdown (5% FDR) in MDA-MB-468 and T47D cells, respectively . These iAltogether, these analyses suggest that ARHGEF17, EAF1, PFKM, and PLA2G16 copy number changes can be linked with changes in YAP signaling. Across the tumor samples, increased expression of ARHGEF17 is associated with increased YAP activity and increased sensitivity to loss of the YAP gene and interestingly is linked with decreased sensitivity to methotrexate J. This eWe propose here a genetic association approach to identify genes whose expression changes can drive differences in kinase signaling. Such regulators are unlikely to be direct, because they are found through genetic association for differences in steady-state phosphorylation levels. We use copy number variation instead of recurrent missense mutations, given that they form a critical mass of genomic aberrations encountered in multiple tumor types and compared with point mutations, the impact of copy number changes on an individual gene is easier to interpret. For most genes, their copy number profiles tend to be well correlated with gene expression data . AlthougWe selected for further analysis several putative regulators of YAP phosphorylation, including ARHGEF17 that we followed up through (phospho)proteomics studies. ARHGEF17 is a Rho guanosine triphosphatase (GTPase) with a functional role in the maintenance of actin cytoskeleton organization and focal adhesion . In the The current study relied on antibody measurement of phosphorylation and protein abundance levels of proteins with reliable antibodies and previous evidence of a role in cancer. This approach has great coverage, allowing us to identify associations from ∼5,600 samples and thus providing reliable significant associations among key signaling changes in cancer. However, our analysis is focused on a restricted set of signaling pathways, and inevitably, we are unable to identify regulators of less studied pathways. More recently, mass spectrometry has started to be used to profile tumor samples on a large scale for an increasing number of samples . AlthougCancer cells often become dependent on changes in the genes that led to the cancer development, a phenomenon dubbed oncogene addiction. When a signaling pathway is observed to be hyperactive in a tumor, relative to healthy tissue, it is often assumed that such activation is a driving event and a requirement for cell viability. Therefore, cellular dependency on kinase activity has been exploited in many targeted therapies . We testpbeltrao@ebi.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Pedro Beltrao (Plasmids generated in this study are available upon request or can be purchased from the named supplier or contact listed as supplying the reagent.PXD021031 (The accession number for the mass spectrometry (phospho-)proteomics reported in this paper is ProteomeXchange Consortium PRIDE: XD021031 .The human breast cancer cell lines - MDA-MB-468 , MDA-MB-361 , T47D , MCF7  and the cervical cancer HeLa cells  were all maintained at 37°C with 5% CO2. All cells were grown in DMEM with 10% FBS. For serum starvation, cells were incubated in DMEM without other supplements for 24 h. All cell lines were authenticated using STR profiling method at the CRUK core facility.http://sherwood.cshl.edu:8080/sherwood/) and cloned into the expression vector pZIP-SSFV-Ultra-Puro_ZsGreen . ShRNA plasmids together with pMDL, pCMV-Rev, and pVSV-G were used to produce virus in 293FT cells. Resulting viruses were used to infect and generate stable cell lines for both MDA-MB-468 and T47D using puromycin selection.ShRNA hairpins were designed using (ARHGEF17_shRNA1 (TGCTGTTGACAGTGAGCGCCAGGAGGTTATTCAGAGCATATAGTGAAGCCACAGATGTATATGCTCTGAATAACCTCCTGATGCCTACTGCCTCGGA)ARHGEF17_shRNA2 (TGCTGTTGACAGTGAGCGACATCACCAAGATGGTATCTGATAGTGAAGCCACAGATGTATCAGATACCATCTTGGTGATGGTGCCTACTGCCTCGGA)PLA2G16_shRNA1 (TGCTGTTGACAGTGAGCGCTGGAGTCATGTTCTCAAGAAATAGTGAAGCCACAGATGTATTTCTTGAGAACATGACTCCAATGCCTACTGCCTCGGA)PLA2G16_shRNA2 (TGCTGTTGACAGTGAGCGCGAACTGCGAGCACTTTGTGAATAGTGAAGCCACAGATGTATTCACAAAGTGCTCGCAGTTCTTGCCTACTGCCTCGGA)HeLa cells were plated on glass cover slides in a 24 well plate at 1e5 cells/plate in DMEM-12 media with 10% FBS. 24 h later cells were transfected with the following plasmids using Lipofectamine 2000 (Thermo Fisher) in accordance with the manufacturer’s instructions; GFP-LATS2 , GFP-ARHGEF17 , Flag-tagged-EAF1 or -PFKM (purchased from GensScript). 24 h post transfection cells were fixed in 4% formaldehyde for 20 m, washed with T-PBS (0.1% Tween/PBS), and permeabilised with 0.1% Triton X-100 for 15 m. After blocking for 1h , fixed cells were incubated with primary antibody to FLAG (F1804/ThermoFisher) and YAP  in blocking buffer for 1h. Following extensive washing with T-BPS slides were incubated with Alexa conjugated antibodies for FLAG and YAP detection and then mounted with Prolong Antifade mounting. Images were captured in a Leica SP5 confocal microscope and processed using Fiji software.For GFP-TEM4/GFP-LATS2 overexpression studies, cells were seeded at low density (50% confluency), in contrast to EAF1/PFKM overexpression studies were cells were seeded at high density (100% confluency). Both transfected and shRNA KD cells were lysed in 50 mM Tris-Cl, pH 7.6; 150 mM NaCl, 1% NP-40 supplemented with Protease inhibitors . Cells lysates were centrifuged for 10 min at 4oC to remove insoluble debris. Lysates were separated using 4%–20% Gradient gels  and immunoblotted using standard protocol. Primary antibodies used were YAP , p-YAP (S127) and GAPDH . Blots were probed with mouse or rabbit specific IRDye 800 (LiCOR) and acquired, and quantified, using Odyssey Classic. Total RNA was isolated and purified from cells using miRNeasy Mini Kit (QIAGEN). cDNA was synthesized using the High Capacity RNA-to-cDNA kit (ABI) according to manufacturer’s instructions. qRT-PCR was performed using Fast SYBR Green Master Mix (ABI) on the QuantStudio 12K Flex Real-Time PCR System (ABI). Relative expression levels were defined using the DDCt method and normalizing to 36B4/RPLP0.(AGGCCATCGTGAAGAAGGAA & CAAAGTGCTCGCAGTTCTCA), primer pair 2 (AGGAGGTGCTCTACAAGCTG & CTCCATAGCGCAGCTCATTC). ARHGEF17: primer pair 1 (GAGAAGTTGAGCCCATGCTG & GTAGGGCCCTTTCAGACTGT), primer pair 2 (GCGGAAGTCCCTGTCAAATC & ACCCTCAGCTCTGAAAGGTC). 36B4/RPLP0: primer pair (GTGTTCGACAATGGCAGCAT & GACACCCTCCAGGAAGCGA)PLA2G16: primer pair 1 Adherent cells were grown to complete confluence prior to harvesting and harvested by scraping on ice in 1X PBS containing protease inhibitors . Cell pellets were dissolved as described previously  and protNormalized copy number (log2) datasets  and RNaseq expression datasets  for 31 different tumor types generated as part of TCGA  consortihttps://depmap.org/portal/achilles/). These datasets were generated by Project Achilles to catalog gene essentiality across cancer cell lines. The RNAi screen (Ach 2.20.2) has high coverage targeting 17,098 genes in 501 cell lines. Out of these, 406 cell lines had DepMap-RPPA data and 166 cell lines had available MCLP-RPPA data  and tumor type as covariates. A separate covariate model was run for each phosphosite.Cy) with expression-based measurements (Ey) in the model.Two models were compared using a likelihood ratio test to identify the effect of a given CNV on phosphosite levels after taking into account the matched protein abundance and tumor specificity. In order to identify associations between gene expression and phosphosite levels, the same approach was repeated after replacing the copy number measurements  effect size of CNV∼Phosphosite levels, ii) effect size of Expression∼Phosphosite levels and iii) the network based score. Short-listed genes for a phosphosite were ordered by their highest average combined rank, calculated using Borda method in R. Genes were iteratively picked with priority given to the highest ranked gene over other genes with strongly correlated copy number profile (r > 0.5), which made them more likely to belong to the same genomic segment.Significant associations identified in tumor samples were also tested in 890 cancer cell lines with RPPA and expression data in DepMap database and 319 cancer cell lines with Expression and RPPA datasets. To evaluate the quality of our associations from the pan-tumor analysis, we predicted the phosphosite levels from expression data in cancer cell lines using models trained using TCGA, MCLP and DepMap data and correlated with the ‘true’ phosphosite activities measured independently in MCLP or DepMap databases.50 values) generated from drug treatment of cancer cell lines were associated with levels of individual phosphosites across these cancer cell lines with the protein abundance of the phosphosite as a covariate in the linear model. The p-values of these drug∼gene associations were adjusted for multiple comparisons and used as a measure of significance.Similar to genetic associations performed between gene copy number and phosphosite levels, associations were also performed between drug screens and phosphosite levels across cancer cell lines using a linear model approach. Briefly, drug response data  providing quantification for 34,716 unique phosphosites in T47D cell line. Similarly, there were 40,754 phosphopeptides  providing quantification for 34,223 unique phosphosites in MDA-MB-468 cell line. The data were quantile normalized for each cell line separately. Differential phosphoproteome analysis between siARHGEF17 and matched siRNA control samples was performed separately for T47D cell line and MDA-MB-468 cell line using the lmFit and the eBayes functions in the limma  package All statistical tests were performed in R. The two-tailed paired t test was applied to analyze differences between control and the positive (or negative) regulators in the knockdown and overexpression experiments. Data are shown as mean + SEM relative to control experiment.


Drosophila stable cell lines has become invaluable for complementing in vivo experiments and as tools for genetic screens. Recent advances utilizing attP/PhiC31 integrase system has permitted the creation of Drosophila cells in which recombination mediated cassette exchange (RMCE) can be utilized to generate stably integrated transgenic cell lines that contain a single copy of the transgene at the desired locus. Current techniques, besides being laborious and introducing extraneous elements, are limited to a handful of cell lines of embryonic origin. Nonetheless, with well over 100 Drosophila cell lines available, including an ever-increasing number CRISPR/Cas9 modified cell lines, a more universal methodology is needed to generate a stably integrated transgenic line from any one of the available Drosophila melanogaster cell lines. Here, we describe a toolkit and procedure that combines CRISPR/Cas9 and theaaa PhiC31 integrase system. We have generated and isolated single cell clones containing an Actin5C::dsRed cassette flanked by attP sites into the genome of Kc167 and S2R+ cell lines that mimic the in vivo attP sites located at 25C6 and 99F8 of the Drosophila genome. Furthermore, we tested the functionality of the attP docking sites utilizing two independent GFP expressing constructs flanked by attB sites that permit RMCE and therefore the insertion of any DNA of interest. Lastly, to demonstrate the universality of our methodology and existing constructs, we have successfully integrated the Actin5C::dsRed cassette flanked by attP sites into two different CNS cell lines, ML-DmBG2-c2 and ML-DmBG3-c2. Overall, the reagents and methodology reported here permit the efficient generation of stable transgenic cassettes with minimal change in the cellular genomes in existing D. melanogaster cell lines.The generation of  Drosophila biology, both in vivo and in vitro tissue culture cells. Over time, the techniques utilized have been modified and refined to increase the efficiency of generating stable transgenic lines  as a dual plasmid system or with dual expression cassettes in a single vector or using a multicistronic vector  in Both the approaches described above have their advantages. With the Ras-attP lines, constitutive GAL4 expression can be leveraged for transgenic expression and the genomes of the Ras-attP lines are not as complex as the other established lines  and CNS (ML-DmBG2-c2 and ML-DmBG3-c2) lines. Eventually, the attP insertion cassette will be expanded to include more of the existing Drosophila cell line collection. Furthermore, all the reagents generated are available to the Drosophila research community to either independently create attP containing cell lines or utilize the created attP lines reported here for RMCE to insert desired transgene of interest.Subsequently, CRISPR-Cas9 based approaches have revolutionized genome editing  on in vivo attP sites demonstrated to produce transformants and having high viability. Upon finalizing the target loci, guide RNA (gRNA) candidates were determined using the tool at http://tools.flycrispr.molbio.wisc.edu/targetFinder/, last accessed 05/19/2019 . For the site at 99F8, a single gRNA was cloned into the pU6-3-gRNA vector  as described  was custom synthesized by Synbio Technologies Inc., NJ. Donor template for the 99F8 exchange cassette was assembled into a pBluescript  vector backbone using Gibson Assembly to create pBS-99F8-5’HR-attP≫Act5C::GFP≪attP-3’HR (DGRC Cat# 1544) vector. The replacement donor was cloned into BsgI digested pBS with the 5’ and 3’ homology arms, which were 0.96 and 1.14 kb, respectively, flanking the attP≫Act::dsRed≪attP insert amplified from pUC57-25C6-5′HR-attP≫Act5C::dsRed≪attP-3′HR. Sequencing confirmed the correct assembly of pBS-99F8-5’HR-attP≫Act5C::GFP≪attP-3’HR vector. The vectors for RMCE, pUC57-attB≫MCS-Act5C::EGFP≪attB (DGRC Cat# 1545) and pUC57-attB≫MCS-MT::EGFP≪attB (DGRC Cat# 1546) were custom gene synthesized by Synbio Technologies Inc., NJ.Drosophila S2R+ , S2 DGRC  cells were grown in M3 media (Sigma) containing Bactopetone, yeast extract and supplemented with 10% fetal bovine serum (M3+BPYE + 10%FBS). Embryonic Kc167  cells were grown in CCM3 (GE Life Sciences) medium. Larval central nervous system derived cell lines ML-DmBG2-c2  and ML-DmBG3-c2  were grown in M3+BPYE + 10%FBS supplemented with 10 µg/ml insulin.Detailed cell culture protocols have been described previously . Briefly6/ml) cells plated in a well of a 6-well dish were transfected with 300 ng of Act::Cas9 (Addgene #62209) and the respective gRNA and repair templates for the 25C6 (75 ng pU6-3-25C6-gRNA1 (DGRC Cat# 1547), 75 ng pU6-3-25C6-gRNA2 (DGRC Cat# 1548) and 150 ng pUC57-25C6-5′HR-attP≫Act5C::dsRed≪attP-3′HR) and 99F8 (150 ng pU6-3-99F8-gRNA (DGRC Cat# 1549) and 150 ng pBS-99F8-5’HR-attP≫Act5C::GFP≪attP-3’HR) loci using Effectene Transfection reagent (QIAGEN) as per the manufacturer’s recommendation. Transfected cells were cultured for 10–20 days to amplify the cultures to two 100 mm dishes before bulk or single cell sorting the cells.For the CRISPR-Cas9 transfections, 2 ml (1.5 × 106/ml) cells plated in a well of a 6-well dish were transfected with 300 ng of either pUC57 attB≫MCS-Act5C::EGFP≪attB or pUC57 attB≫MCS-MT::EGFP≪attB and 200 ng Act::phiC31 integrase (DGRC Cat # 1368), using Effectene Transfection reagent (QIAGEN) as per the manufacturer’s recommendation. Transfected cells were cultured for 10–20 days to amplify the cultures to two 10 cm dishes before bulk sorting the cells.For RMCE transfections, 2 ml  isolation. The harvested cells were centrifuged and washed once with phosphate-buffered saline (PBS). Genomic DNA was extracted from the PBS washed pellet using the Zymo Quick-DNA™ MiniprepPlusKit (D4068/4069). PCR reactions were performed to diagnose and confirm CRISPR-Cas9 mediated homologous recombination , allowed to attach for 1–2 hours, fixed with 4% paraformaldehyde in PBS, pH 7.0, washed and immunostained with rabbit Anti-dsRed  and/or mouse Anti-EGFP (DHSB 12A6) antibodies and the respective Alexa dye-coupled secondary antibodies (Jackson ImmunoResearch) reconstituted in 5% Normal Goat Serum, 0.1% Triton in PBS. Cells were then mounted on DAPI containing mounting media (Vectashield H-1200) prior to imaging on a Leica SP8 confocal microscope.https://doi.org/10.25387/g3.14551074.All data necessary for confirming the conclusions in this paper are included in this article and in supplementary figures and tables. All cell lines and plasmids are available from the DGRC. Supplementary material is available at figshare: D. melanogaster lines had the highest survival rates of transformants listed in the Rainbow Transgenics website. Amongst the lines that were positive for transgenic transformants, the insertion at 99F8 on 3 R had the highest survival rate . Despite the low percentages of the GFP- and double-positive fractions, it was feasible to expand these fractions to an extent that cassette exchange at the 99F8 locus  could be confirmed and sequence verified.Constitutive overexpression of certain transgenes in cell culture could potentially be toxic leading to cell death. This can be circumvented by the use of an inducible metallothionein (MT) promoter (It is evident from the data presented that the inserted attP site(s) in the S2R+-attP-99F8 Clone 141 line are functional. Taken together, it is most likely that this line has at least 2–3 attP insertions at the 99F8 locus given the number of copies of the 3rd chromosome in S2R+ cells . To generate the attP insertion at the 25C6 locus using CRISPR-Cas9 strategy, cells were transfected with two gRNAs accompanied by the repair construct containing the attP≫Act::dsRed≪attP cassette flanked with the homology arms. Cells positive for dsRed were enriched by fluorescence cell sorting at least 10–20 days after the initial transfection before sorting to derive SCCs. The integration of the donor repair cassette was assessed by PCR amplification across the integration locus from outside the homology arms . A successful insertion yielded a 5.7 kb amplicon while a 2.6 kb amplicon was observed if the 25C6 genomic locus remained unmodified. A nonhomozygous modification of the locus would be expected to yield both bands, while a homozygous genomic modification would only yield a single 5.7 kb amplicon (Band 1) . We further used primer pairs that amplified from within the insert to outside the homology arms  to verify the clones and also sequence for the presence of the attP sites . This PCR diagnosis indicated that we obtained a homozygous S2-DGRC clone containing the attP cassette at the 25C6 locus .D. melanogaster cell lines, we introduced attP sites at 25C6 and 99F8 in other cell lines. We documented the homozygote ML-DmBG2-c2-attP-25C6-Clone 8  and ML-DmBG3-c2-attP-25C6-Clone 74 , Kc167-attP-25C6-Clone 35  and the heterozygote Kc167-attP-99F8-Clone 71 , and ML-DmBG3-c2-attP-99F8 28 . We summarize the various efficiencies of the genomic modification process and single cell cloning with different cell lines in In order to investigate the applicability of this technique to other D. melanogaster cell lines. All the reagents generated in this study will be accessible to researchers from the Drosophila Genomics Resource Center to create their own cell lines, as well as a cost-recovery service to aid with deriving clonal populations post-RMCE. Overall, these cell lines and plasmid collection created will aid researchers to further harness D. melanogaster cell culture to address broader research questions.In summary, we have been able to insert RMCE docking cassettes containing flanking inverted attP sites in two different genomic loci in both embryonic and larval CNS derived cell lines. Successful cassette exchange was demonstrated in the generated S2 and Kc167 attP lines. Moreover, the methodology presented here not only permits the introduction of attP sites at loci 25C6 and 99F8 in any cell line but also with a few minor modifications of the reagents generated in this study, an attP site could be inserted in any desired locus. Interestingly, our results demonstrate that the insertion of an attP site in every copy of the genome and RMCE conversion is variable. Nonetheless, this “heterozygosity” would enable researchers to generate a multitude of clones that could have multiple transgenes within the same clonal line or express varying doses of the transgene with the option to choose the directionality of the exchanged cassette. Given the numerous polyclonal variants that are observed in the bulk-sorted post-RMCE population, we strongly recommend deriving a single cell population. Our approach also permits the comparisons of transgenic expression from the same locus across various biological and physiologically diverse