Microcystis aeruginosa and the algal organic matters (AOMs) produced by M. aeruginosa, the combined process of nanoscale zero-valent iron (NZVI) and hydrogen peroxide (H2O2) has been carried out, and the removal mechanism has also been clarified. As the initial cyanobacterial cell concentration is 1.0 (±0.05) × 105 cells·mL−1, all the treatments of NZVI, H2O2, and NZVI/H2O2 have inhibition effects on both the Chl a contents and photosynthetic pigments, with the Chl a removal efficiency of 47.3%, 80.5%, and 90.7% on the 5th day, respectively; moreover, the variation of ζ potential is proportional to that of the Chl a removal efficiency. The malondialdehyde content and superoxide dismutase activity are firstly increased and ultimately decreased to mitigate the oxidative stress under all the treatments. Compared with NZVI treatment alone, the oxidation of the H2O2 and NZVI/H2O2 processes can effectively destroy the antioxidant enzyme system and then inactivate the cyanobacterial cells, which further leads to the release of photosynthetic pigments and intracellular organic matters (IOM); in addition, the IOM removal efficiency (in terms of TOC) is 61.3% and 54.1% for the H2O2 and NZVI/H2O2 processes, respectively. Although NZVI is much more effective for extracellular organic matters (EOM) removal, it is less effective for IOM removal. The results of the three-dimensional EEM fluorescence spectra analysis further confirm that both H2O2 and NZVI/H2O2 have the ability to remove fluorescent substances from EOM and IOM, due to the oxidation mechanism; while NZVI has no removal effect for the fluorescent substances from EOM, it can remove part of fluorescent substances from IOM due to the agglomeration. All the results demonstrate that the NZVI/H2O2 process is a highly effective and applicable technology for the removal of M. aeruginosa and AOMs. In order to eliminate the harmful cyanobacterium  Microcystis aeruginosa has received increasing attention in recent years due to the production of hazardous cyanotoxins and secretion of algal organic matters (AOMs) , 2O2, and NZVI/H2O2, respectively, and increase to 36.1%, 40.9% and 49.2% on the 5th day, respectively, indicating the PC could be suppressed by all treatments. It is observed that the inhibition effects of PC are similar to that of the Chl a content. However, the results show the suppression effects of APC and PE are different from PC  is mainly due to the decomposition of H2O2 or the consummation by the Fenton-like reaction. These results indicate that the oxidation of H2O2 or NZVI/H2O2 is good for the removal of PC and APC, while ZNVI is the best for PE removal. Photosynthetic pigments are widely used for monitoring algal and cyanobacterial photosystem II (PSII) activity . As show from PC c,d. When removed c. In addZVI/H2O2 d, demons2O2 also plays an important role in the removal performance [2O2 can greatly improve the cyanobacterium M. aeruginosa removal [M. aeruginosa by the NZVI/H2O2 process is mainly due to the oxidative effect of H2O2 and, secondly, due to the agglomeration of NZVI. The agglomeration process, which may facilitate cyanobacterial precipitation, has been investigated by many researchers [Previous studies have concluded that the microalgae removal by NZVI is size-dependent and dosage-dependent, and it is due to the agglomeration and physical interactions ,13,30. Iformance ,31. More removal . Obviousearchers ,13,24,32earchers ,13. 2O2 on the zeta potential of M. aeruginosa are presented in 2O2, and NZVI/H2O2 are −0.17 ± 0.05, −0.07 ± 0.00, and −2.73 ± 0.14 mV on the 1st day, respectively. With the extension of exposure times, the ζ potentials for all treatments are considerably lower than the control after being exposed for 120 h, which are −6.07 ± 0.10, −4.17 ± 0.14, and −3.20 ± 1.20 mV, respectively. Interestingly, compared with the first day, the M. aeruginosa cells for the control have a highly negative charge on the 5th day, with a ζ potential of −0.23 ± 0.05 mV. This is most likely because more EOM is produced, and the surface properties of the cyanobacterium are changed, during the cyanobacterial growth [a removal efficiency, that is, the higher the removal efficiency is, the higher the ζ potential [2O2 is also found to affect the cell surface properties and change the membrane potential of cyanobacterial cells [a removal efficiencies are increased with the increasing of ζ potentials , which is demonstrated by the variation of the MDA contents [M. aeruginosa, and the reduced photosynthetic activity leads to the accumulation of ROS and the inhibition growth of cyanobacterium; on the other hand, H2O2 or NZVI/H2O2 generate hydroxyl radicals and destroys cyanobacterial membrane systems by the oxidation, which then results in the decrease in SOD, POD, and CAT activities. Additionally, the results of the combined NZVI/H2O2 process demonstrate that NZVI agglomeration behavior promotes cyanobacterial removal, but oxidation is still the dominant function in the entire combined inhibition effect. Generally, both iron (high concentration) and Hruginosa ,15,31,37contents ,25. On tM. aeruginosa, may pose a threat to human health, especially in the drinking water treatment that disinfects with chlorine-containing disinfectant [M. aeruginosa to different treatments results in EOM and IOM variations. As 2O2 and NZVI/H2O2 treatment on the 5th day, respectively, while the IOM removal efficiency is 11.7%, 61.3%, and 54.1%, respectively  and H2O2/Fe(III) [−1 after being treated by PMS/Fe(II) [2O2 and NZVI/H2O2 processes cause massive damage to the cell structure of M. aeruginosa, which are collaborated by the results of the photosynthetic pigments’ removal   and decr removal . MoreoveM. aeruginosa [2O2 are distinctly different. For EOM fractions, there are three major fluorescent peaks in both CK and NZVI treatments: peak A represents soluble cyanobacterial metabolic byproducts with the Ex/Em of 278 nm/335 nm; peak B (Ex/Em of 314 nm/400 nm) and peak C (Ex/Em of 255 nm/406 nm) stand for fulvic-like acids (2O2 or NZVI/H2O2 treatments (2O2 (NZVI/H2O2) treatment are nearly unanimous and much lower than those observed for the CK and NZVI treatments  and peak D (Ex/Em of 282 nm/313 nm) represent soluble cyanobacterial metabolic byproducts, peak E (Ex/Em of 364 nm/451 nm) and peak F (Ex/Em of 272 nm/451 nm) represent fulvic-like acids, and peak G (Ex/Em of 232 nm/331 nm) represents aromatic proteins II e,f. In ceatments g,h, whicensities . AOMs from cyanobacteria are a kind of complex compound, containing polysaccharides, amino acids, proteins, peptides, organic acids, lipids, fatty acids, nucleic acids, cyanobacterial toxins, and so on ,39. This2/Fe(II) on IOM indicates that the fluorescence intensities of the humic acid-like material (Ex/Em of 250~330 nm/380~375 nm) and soluble microbial products (Ex/Em of 280/330 nm) are extensively decreased with the CaO2 dosage increasing [However, numerous studies have shown that the combined processes of oxidation and coagulation can cause the disruption of cyanobacterial cells and the release of IOM ,20,28. Acreasing ; all the2O2 [2 [2O2 possesses a much stronger removal efficiency, which is improved by Fe(II) or Fe(III) coagulation [2O2 are simultaneously dosed into the cyanobacterial solution, it is speculated the cyanobacterial cells are firstly agglomerated by NZVI, and then Equations (2)–(6) are expected to happen as chemical oxidation processes, with Fe(III) likely formed to effectively remove cyanobacterial cells and AOMs [2+ (Equation (7)) and HO• (Equation (8)) [M. aeruginosa behind such procedures is attributed to the alteration of the cyanobacterial surface zeta potential [2O2/Fe(II) and H2O2/Fe(III) processes could reduce the M. aeruginosa cells, allowing the cyanobacterial cells to settle down more easily with Fe(II) or Fe(III) [AOPs are used worldwide as a feasible strategy to eliminate harmful cyanobacteria . Strong 2O2 ,31, CaO22O2 [2 , PMS [202O2 [2  have shoand AOMs . Subsequions (2)– are expeotential , and the2O2/Fe(III) process [M. aeruginosa could be removed efficiently by oxidation and coagulation. Based on previous studies and current results, it can be deduced that the combined effect of NZVI/H2O2 on M. aeruginosa cells is likely caused by the agglomeration and oxidation process. The oxidation, which more easily releases photosynthetic pigments and IOM, may be the main cause of the destruction of antioxidant enzyme system, the removal of AOMs, and the rupture of cyanobacterial cells.In our study, the enhanced removal performance of cyanobacterial cells and AOMs indicates that the NZVI is converted into Fe(II) and then Fe(III), which has a more reactive surface area and higher settling property than NZVI. These results are in accordance with the scanning electron microscope images under the H process  and PMS/ process , demonst2O2, as well as of H2O2/Fe(II) and H2O2/Fe(III) [2O2 should be further considered. Notwithstanding its limitations, the present study suggests that the harmful cyanobacterium M. aeruginosa and AOMs could be effectively removed by the NZVI/H2O2 process.The oxidation process of NZVI/H/Fe(III) , has bee/Fe(III) ,13; more2O2 on the representative toxic cyanobacterium M. aeruginosa is investigated in the present study. All the treatments of NZVI, H2O2, and NZVI/H2O2 have inhibition effects on both the Chl a contents and photosynthetic pigments, with the Chl a removal efficiency of 47.3%, 80.5%, and 90.7% on the 5th day, respectively; moreover, the variation of ζ potential is proportional to that of the Chl a removal efficiency, that is, the higher the removal efficiency is, the higher the ζ potential. The malondialdehyde content and superoxide dismutase activity are firstly increased and ultimately decreased, to mitigate the oxidative stress under all the treatments. Compared with single NZVI or H2O2, NZVI/H2O2 is much more efficient for removing M. aeruginosa through the simultaneous agglomeration and oxidation process. The oxidation of the H2O2 and NZVI/H2O2 processes can effectively destroy the antioxidant enzyme system and then inactivate the cyanobacterial cells, which further leads to the release of photosynthetic pigments and IOM; in addition, the IOM removal efficiency (in terms of TOC) is 61.3% and 54.1% for the H2O2 and NZVI/H2O2 processes, respectively. NZVI is much more effective for EOM removal, but it is less effective for IOM removal. The results of the three-dimensional EEM fluorescence spectra analysis further confirm that both H2O2 and NZVI/H2O2 have the ability to remove fluorescent substances from EOM and IOM due to the oxidation mechanism; while NZVI has no removal effect for fluorescent substances from EOM, it can remove part of the fluorescent substances from IOM due to the agglomeration. All the results demonstrate that the NZVI/H2O2 process is a highly effective and applicable technology for the removal of M. aeruginosa and AOMs.The combined effect of NZVI and H


Therefore, we explored the nutritional deprivation in a mouse model of glioma. Animals subjected to a cysteine/cystine‐free diet survived longer, although this increase did not attain statistical significance, with concomitant reductions in plasma glutathione and cysteine levels. At the end point, however, tumors displayed the ability to synthesize glutathione, even though higher levels of oxidative stress were detected. We observed a compensation from the nutritional intervention revealed as the recovery of cysteine‐related metabolite levels in plasma. Our study highlights a time window where cysteine deprivation can be exploited for additional therapeutic strategies.Nutritional intervention is becoming more prevalent as adjuvant therapy for many cancers in view of the tumor dependence on external sources for some nutrients. However, little is known about the mechanisms that make cancer cells require certain nutrients from the microenvironment. Herein, we report the dependence of glioma cells on exogenous cysteine/cystine, despite this amino acid being nonessential. Using several  Herein, we demonstrate that glioma cells rely on cysteine/cystine from the microenvironment for glutathione synthesis needed to fight reactive oxygen species, due to their inability of diverting methionine‐derived metabolites toward cysteine synthesis. We show that via nutritional intervention alone, we can extend the survival of a mouse model of glioma, suggesting a time window for this type of intervention clinically. BSObuthionine sulfoximineCBScystathionine β‐synthaseCTHcystathionine gamma‐lyaseCyscysteineCysscystineFDRfalse discovery rateGCLCglutamate–cysteine ligase catalytic subunitsGCLMglutamate–cysteine ligase modifier subunitGlnglutamineGSHglutathioneHRPhorseradish peroxidaseIDH1isocitrate dehydrogenase 1LC/MSliquid chromatography/mass spectrometryPAGpropargylglycineROSreactive oxygen speciesSAHS‐adenosylhomocysteineSAMS‐adenosylmethionineSCIDsevere combined immunodeficientTCAtricarboxylic acid cycleTStranssulfuration1The metabolism of tumor cells depends on many factors, notably, nutrient availability from the microenvironment and oncogenic mutations. Others have studied whether restricting the nutrient availability from the microenvironment can reduce the distinctive high proliferation rate of Myc‐driven tumors, which require glutamine , 2, 3, oGliomas are brain tumors that can have an aggressive phenotype and currently have no curative treatment. The potential of incorporating a dietary plan into the treatment of this disease is highly desirable for patients. In clinical studies of gliomas, one of the most popular diet as adjuvant therapy is the ketogenic diet , with ovGliomas harboring isocitrate dehydrogenase 1 (IDH1) mutations  have beeS‐adenosylmethionine (SAM) and S‐adenosylhomocysteine (SAH). Subsequently, homocysteine can either be diverted into cysteine synthesis through the TS pathway or be remethylated to yield methionine and generate tetrahydrofolate. Hepatic tissue synthetizes almost half of the total pool of GSH from methionine‐derived cysteine ‐13C‐cystine  were utilized for 13C tracing. Experiments involving methyl‐13C‐methionine , [C3‐13C]‐serine , and [U‐13C]‐glutamine  were performed in DMEM:F12 using in‐house made medium that lacked the metabolites utilized as 13C tracers.Medium lacking cysteine and cystine was provided by Thermo Fisher  upon request. ‐glutamine was prepared as a 36.2 mg·mL−1 stock solution in sterile PBS and injected  into the tail vein at 15‐min intervals for three times  just prior to mice reaching end point. Mice were euthanized 15 min after the last injection (45 min from the first injection). Tumors were separated from the brain, then both tumor and normal tissues were gently blotted by rapidly tapping the tissue onto a cloth and were immediately flash‐frozen in liquid nitrogen.escribed , 28 on t2.18g, and the resulting upper hydrophilic phase was then transferred to a clean vial and dried under a stream of N2. Dried sediments were resuspended in methanol and injected into the LC/MS system for global profiling.Blood was collected from the tail vein of the mice in lithium–heparin collection tubes . Approximately 35 µL of blood was centrifuged, according to the tube manufacturer’s instructions, at 4 °C, and the clear plasma fraction was transferred to a clean microtube. Subsequently, plasma was extracted in a water : methanol : chloroform mixture, centrifuged for 20 min at 4 °C and 23 240 2.19imagej software .When mice reached end point, tissue was collected and stored in paraformaldehyde at 4 °C. Tissue was submitted to Histoserv, Inc. for analysis together with the antibodies. Slides were deparaffinized and hydrated through graded alcohols to distilled water, followed by antigen retrieval. They were then blocked with hydrogen peroxide and a blocking serum. Next, the slides were incubated with the primary antibody, a secondary antibody, and HRP‐conjugated streptavidin. Finally, the slides were developed using 3,3′‐diaminobenzidine and counterstained with hematoxylin. All the incubations were carried out at room temperature, with Tris‐buffered saline + Tween‐20 used as a washing buffer. Immunostaining was quantified using 2.20graphpad prism v7.05 (GraphPad Software). Multivariate analysis was performed using metaboanalyst 4.0 ‐glutamine to track its incorporation in downstream metabolites. We observed an accumulation of m+5 glutamine  DNA methylation‐based tumor classification and copy number variation plot of glioma cell lines including the classification scores arising from their methylome profile. NCH1681 cell lines was matched to high grade IDHmut astrocytoma with a calibrated score of 0.94 while BT142 with a calibrated score of 0.99. TS603 was not matched to high grade astrocytoma. Copy number variation shows loss of 1p and 19q and with the presence of IDH1mut, this cell line is classified as a IDH1mut oligodendroglioma 1p/19q co‐deleted. (B) Detection of IDH1 mutation in glioma cell lines by sequencing analysis. (C) Western blot of IDH1 mutant enzyme. (D) LC‐MS quantification of D2HG  including a wild type glioma cell line (GSC923) as negative control.Fig. S2. (A) Sphere formation of glioma cell lines after 96 hours growing in media with/out cysteine/cystine. Representative pictures are shown. (B) Western blots of glutamate cysteine ligase modulatory subunit (GCLM) for 3 IDH1 mutant glioma cell lines grown in media without cysteine/cystine and treated with 0.1 mM cystathionine or homocysteine. (C) Normalized intensity  of the proteins bands relative to α‐tubulin expression from (D) western blots of p‐GCN2 and p‐eIF2α for 3 IDH1 mutant glioma cell lines grown in media without cysteine/cystine. (E) DCFDA intensity plots as marker of ROS in glioma cell lines under cysteine/cystine‐deprivation and treated with 0.25 mM Trolox including the relative quantification of positive‐stained cells . (F) Viability of glioma cell lines in cysteine/cystine‐lacking media supplemented with either GSH or GSH‐ethyl ester (GSH‐E)  and (G) quantification of intracellular GSH . (H) Viability of glioma cell lines after 96 hours in media lacking cysteine and cystine and in the same media but treated with 2 µM ferrostatin 1. . (I) Quantification of the number of cells assigned to early apoptosis and apoptosis  from the apoptosis detection assay diagrams at 96 hours and (J).Fig. S3. (A) Relative intensity of the CBS and CTH bands normalized to β‐actin expression  from the western blots of CBS and CTH for 3 IDH1 mutant glioma cell lines grown in media without cysteine/cystine. (B) Metabolic effect of propargylglycine (PAG) due to inhibition of the TS pathway. (C) Volcano plot displaying the Log2(FC) vs Log for all the metabolites identified by LC‐MS. Metabolites highlighted in green (upregulated upon PAG treatment) or red (downregulated) have Log2(FC) > 1 or < −1 and an FDR p value < 0.05 (5 replicates per cell line and per condition), in addition to glutathione (GSH).Fig. S4. Levels of (A) tetrahydrofolate (THF) and folate, (B) Relative quantification of the expression of MTHFD1 as the normalized intensity of its band to β‐actin  and (C) citrulline and arginine for the 3 cell lines in full and cysteine/cystine‐lacking media for 48 hours. Metabolite levels are computed from the unlabeled global profiling experiment .Fig. S5. (A) Effect of BSO plus tBH on viability. Data are mean ± SD of viability values normalized to the control conditions (full medium), n = 3; *p < 0.05, **p < 0.005, ***p < 0.001, 2‐way ANOVA followed by Dunnet’s multiple comparison test for control vs. all). (B) Quantification of GSH levels after treatment with 250 µM BSO. (C) Normalized intensities of cysteine‐related metabolites in plasma collected from a mouse model of glioma and normalized to those computed for the control group .Click here for additional data file.


HML-6, at 19q13.43b in glioblastoma cells. HERV expression inversely correlated with loci-specific DNA methylation. HML-6 contains an intact open reading frame encoding a small envelope protein, ERVK3-1. Increased expression of ERVK3-1 in GBM patients is associated with a poor prognosis independent of IDH-mutational status. Our results suggest that not only is HML-6 uniquely overexpressed in highly invasive cell lines and tissue samples, but also its gene product, ERVK3-1, may be associated with reduced survival in GBM patients. These results may have implications for both the tumor biology of GBM and the role of ERVK3-1 as a potential therapeutic target.Comprising approximately 8% of our genome, Human Endogenous RetroViruses (HERVs) represent a class of germline retroviral infections that are regulated through epigenetic modifications. In cancer cells, which often have epigenetic dysregulation, HERVs have been implicated as potential oncogenic drivers. However, their role in gliomas is not known. Given the link between HERV expression in cancer cell lines and the distinct epigenetic dysregulation in gliomas, we utilized a tailored bioinformatic pipeline to characterize and validate the glioma retrotranscriptome and correlate HERV expression with locus-specific epigenetic modifications. We identified robust overexpression of multiple HERVs in our cell lines, including a retroviral transcript,  In most differentiated cells, HERVs have lost their innate ability to form active viruses and replicate due to a combination of single nucleotide polymorphisms, coding deletions and mutations, but may nevertheless play roles in multiple physiological processes, including stem cell pluripotency, cell proliferation, and cell survival largely through their cis-regulatory elements found in their LTRs2.Human endogenous retroviruses (HERV) are ancestral remnants of previous germline retroviral infections and account for 8% of the human genome. HERV elements are identifiable by the presence of established retroviral genes: 6. While these HERVs may also be expressed in healthy tissue, HERVs have been implicated in a number of disease processes, including autoimmune conditions and neurological disorders10. Furthermore, in cancer cells with known epigenetic dysregulation, HERVs have been implicated as potential oncogenic drivers12. For example, loss of CpG methylation in oncogenesis appears to impact HERV expression13. HERVs may contribute to cancer progression in several ways, including cis-activation of oncogenic promoters, direct protein formation or cell fusion14. HERV envelope proteins have been particularly implicated in multiple disease processes, including germ cell tumors and pancreatic cancer, suggesting that HERVs and their potential protein products may be associated with oncogenesis16.However, several HERVs  maintain variable degrees of transcriptional activity that are typically regulated by epigenetic mechanisms, including chromatin remodeling, DNA/histone methylation of transcription factors, and retroviral LTRs18. Given the extent of HERV expression in many cancer cell lines and the known epigenetic dysregulation in gliomas, here, we conduct an exploratory analysis of three separate glioma cell lines  to characterize their retrotranscriptome, locus-specific gene expression, and differential methylation profiles. We sought to identify locoregional differential expression of endogenous retroviral elements, tumor suppressors and oncogenes in gliomas. Additionally, we investigated the link between HERV expression and locus-specific DNA methylation. Furthermore, we demonstrate that increased expression of one HML-6 locus and its gene product, ERVK3-1, are associated with reduced survival in GBM patients.In gliomas, HERV-K (HML-2) expression has been detected in specific tumor samples and cell lines through reverse transcriptase-qPCR; however, the extent and depth of HERV expression in gliomas has not been established to dateHML-6 family had the highest expression , followed by ERV316A3 ; while MER61 (ERV1 superfamily), PABL-A , and PRIMA4 (PRIMA superfamily) were all significantly under-expressed relative to astrocyte controls . The M059J cell line demonstrated significant underexpression of the ERV316A3 (ERV3 superfamily), ERVLE (ERVL superfamily), HERV9, HERVE, and MER101 (ERV1 superfamily) , and significant overexpression of HERV4, HERVL, HML3 (HERVK superfamily), and HERVH families . The H4 cell line similarly demonstrated significant underexpression of the ERV316A3 (ERV1), HERVE, and HERVL . The H4 cell line showed significant overexpression of HERVIP10FH (ERV1), HML3 (HERVK), and MER61 . These findings are summarized in Fig. Results of the HERV expression analysis were stratified by the ten most-dysregulated HERV families in each cell line. In the A172 cell line, the p < 0.0001) and H4 cell lines . There were no significant differences in mean HERV expression between the M059J cell line and H4 cell line , though mean HERV expression in the M05J line was significantly higher relative to astrocyte controls . Mean HERV expression in the H4 cell line was not significantly different from mean HERV expression in astrocyte controls . A summary of these findings is shown in Fig. We analyzed differences in mean expression of overall counts among our cell lines of interest using one-way ANOVA under multiple comparison correction conditions. The results of this analysis demonstrated that the A172 cell line had significantly higher mean overall HERV expression relative to the M059J  and H4 cells . There were no significant differences in the mean number of CpG islands between M059J and H4 cell lines (p > 0.05). This data is summarized in Fig. Analysis of CpG island methylation frequency (via ANOVA with multiple comparisons correction) in each cell line demonstrated that A172 cells had significantly lower mean number of CpG islands relative to M059J cells , as shown in Fig. HML-6 overexpression in A172 correlated with hypomethylation at the HML-6 (19q43b) locus relative to astrocytes (p < 0.0001) as summarized in Supplementary Fig. We assessed the number of differentially methylated regions containing HERVs with a FC > 100 in each cell line. In the A172 cell line, 11 differentially methylated regions contained HERV loci, from an initial group of 100 loci. In the M059J cell line, 4 differentially methylated regions had HERV loci from an initial pool of 30 loci. In the H4 cell line, the 4 differentially methylated regions containing HERV loci were identified from an initial collection of 48 loci. When data from each cell line were pooled, our summative analysis demonstrated a significant inverse correlation between mean beta value and FC HERV expression  confirmed robust expression of the HML-6 locus (A172 > GBM43 > GBM28) and support our bioinformatics findings , the rate-limiting enzyme in cytosine nucleotide biosynthesis, was upregulated in our analysis and overlapped downregulated PAB1XY-like sequences .In addition to our identification of differentially methylated loci, we identified several similarly dysregulated genes log2FC > 1.2). This analysis generated putative differentially expressed gene-HERV (DEG-HERV) pairs in the A172 and M059J cell lines, as shown in Supplementary Table .2. This ERV316A3, and the RNA helicase DDX25 were both over-expressed in the A172 cell line at the 11q24.211 locus. DDX25 is a member of the DEAD-box protein family and is required for RNA processing/export, transcription, and translation initiation. A summary of overlapping DEGs and their functions are also shown in Supplementary Table Similarly, several DEGs found in the A172 cell line were directly involved in cell division and growth. Most notably, the retroviral element, IDH wild-type (IDHwt) GBM patients with ERVK3-1 expression greater than 3.3 fragments per kilobase of transcript per million mapped reads (fpkm) had significantly lower median overall survival (OS) relative to patients with ERVK3-1 expression less than 3.3 fpkm . This trend was preserved when including patients with IDH mutant (IDHm) GBM as well . These findings are summarized in Fig. p = 0.018), as shown in 5C. Among IDHwt patients alone, a similar trend was seen, though these differences were non-significant .Analysis of TCGA and HPA data demonstrated that p < 0.0001), as did patients with 1p19q co-deletion . These findings are shown in Fig. p < 0.0001) and IDHm GBM samples . These findings are summarized in Fig. In addition, patients with IDH mutant GBM had significantly lower ERVK3-1 expression relative to patients with IDHwt tumors  and contains at least one open reading frame (ERVK3-1) and several pro-viral loci12. Previous studies using microarrays have suggested that extensive transcriptional activity of HML-6 exists in a wide variety of normal tissues, including the brain19. However, increased expression of HML-6 pro-viral loci has also been noted in several cancers, including breast cancer and melanoma21. Specifically, small antigenic peptides from the HML-6 env gene (HERV-K-MEL) have been isolated from melanoma tissues, which are recognized by cytotoxic T-cell lymphocytes22. Similar studies have described low-level transcriptional activity of LTR retrotransposons in normal tissues, and subsequent clonal expansion of these retroelements in tumors23. Prior research has also demonstrated that unique HERVs may drive oncogenesis, and that clusters of phylogenetically distinct HERVs may be common among different cancers, as shown by Steiner et al. However, while the authors did note multiple members of the HERV-K family to be dysregulated in their analysis, HML-6 was not described24.The highly proliferative A172 cell line demonstrated a marked overexpression of one specific HERV: HML-6 in cancer, but its oncogenic potential has not been well-defined. Since HML-6 was markedly overexpressed at one specific locus (19q13.43b) in the A172 cell line, we focused on validating our bioinformatics work on this locus. This locus also contains the open reading frame for HML-6 protein , corresponding to its env gene. Our qPCR validated increased transcript expression in both the adherent A172 glioma cell line and neurosphere patient-derived glioma lines (GBM28 and GBM43) compared to normal human astrocyte cell lines. Due to this association, further investigation into the oncogenic potential of HML-6 may be warranted. Currently, there are no commercially available antibodies to the putative HML-6 env protein hence protein expression could not be confirmed.The discovery of these cancer-specific epitopes suggested a role of p = 0.01). Previously, HERV expression has been inversely correlated to DNA methylation in several cancers28. In normal differentiated cells, transcriptional regulation of HERVs is tightly controlled by DNA methylation; however, in embryonic stem cells and some cancers, HERV expression also relies on other epigenetic modifications such as histone methylation (H3K9me3) and acetylation  in head and neck cancer patients compared to adjacent normal tissues30. Similar to our findings, loss of CpG island methylation has also been associated with increased HERV and LINE-1 element expression in ovarian cancer and embryonal cancer cell lines32. This relationship has been further defined in several cancers where exposure to demethylating agents (5-Azacytidine) induces expression of endogenous retroviral elements34.Our analysis suggests an inverse relationship between HERV expression and DNA methylation at several differentially expressed HERV loci across all cell lines  comprises a unique subset of gliomas with a distinct epigenetic landscape. G-CIMP gliomas possess marked DNA methylation and upregulation of key histone methyltransferases and deacetylases that result in global transcriptional silencing, which may silence retroelement expression. As gliomas progress to higher grades, loss of the G-CIMP phenotype has been described, suggesting a potential link between HERV expression and glioma progressionIDH mutant GBM patients had relatively lower ERVK3-1 expression; given that HERV expression is closely linked to epigenetic dysregulation, the high degree of methylation seen in IDHm GBM may explain this finding37. The results of our study have several implications in both the general tumor biology of GBM and in clinical applications as well. Primarily, we suggest that HML-6 may be associated with GBM invasiveness, given its overexpression in our more invasive A172 cell line and patient-derived neurospheres. The potential role of HML-6 as a driver of GBM oncogenesis may center on its unique gene product, ERVK3-1. While multiple HML-6 sites are present in the genome, only the locus at 19q13.43b contains an intact reading frame for ERVK3-1. Our most salient future studies will focus on elucidating the mechanism underlying HML-6 driven oncogenesis and the potential impact ERVK3-1 may have in this process. One potential role ERVK3-1 may have in oncogenesis is immunomodulation21. For example, HML-6 gene products have been previously implicated in immunomodulation, as demonstrated by Schiavetti et al. Their results demonstrated that the HERV-K-MEL envelope protein (located on chromosome 16) may be an epitope for cytotoxic lymphocytes (CTL) against melanoma. Thus, future studies investigating the relationship between ERVK3-1 expression and the tumor immunophenotype would provide critical insight in this regard.Our analysis of ERVK3-1 expression in clinical samples has demonstrated a potential survival detriment in patients with elevated ERVK3-1 expression, marking the first such analysis of a HERV transcript as a negative prognostic marker in GBM. Given the heterogeneity of GBMs, Our subgroup analysis confirmed heterogeneous expression of ERVK3-1 in several GBM subtypes with the highest expression in the classical GBM patients. The classical subtype accounts for approximately 25% of malignant gliomas, and is characteristically marked by Epidermal Growth Factor Receptor (EGFR) amplification/mutation. In concordance with our findings of overexpressed HML-6 expression in GBM cell lines, the two patient-derived neurosphere lines were both derived from patients harboring the classical GBM subtype. In addition, HML-6 and ERVK3-1 with respect to tumor biology may present avenues for assessing ERVK3-1 as a drug target.Clinically, the impact of ERVK3-1 on patient survival from curated tissue samples is intriguing. While our work has formed the basis for ERVK3-1 to be considered a potential prognostic marker for GBM survival, development of a curated database with tumor samples sequenced for HERV and ERVK3-1 expression would be highly beneficial for future clinical application of our findings. In addition, obtaining data from paired primary and recurrent GBM samples would grant further insight into the role of HML-6/ERVK3-1 in tumor recurrence. Further analysis of the role of Our analysis aimed to validate previously described bioinformatic pipelines in established commercially available glioma cell lines with whole-cell RNA-seq data. Although we demonstrated several overexpressed loci, each cell line demonstrated heterogeneous HERV expression, suggesting the absence of one overarching HERV family. Therefore, it remains essential to analyze the retrotranscriptome of each glioma cell line separately. Additionally, several studies have suggested that adherent glioma cell lines do not accurately recapitulate the glioblastoma disease phenotype. Therefore, utilizing patient-derived glioma stem-like cells offers a potential option for uncovering novel HERV transcripts in gliomas.HML-6 in several glioma cell lines; however, we cannot make any conclusions on other glioma cell lines based on our analysis. In addition, RNAseq reads were mapped to the human genome as opposed to the cell line genomes. As these cell lines genomes fundamentally differ from the standard human genome, this may have similarly affected our analysis of HERVs in our cell lines of interest. Given the heterogeneity of gliomas, a tailored bioinformatic pipeline for each cell line may help define the retroviral landscape for each patient. Future investigations could focus on the mechanistic interactions between the DEG-HERV pairs to validate whether certain HERVs alter functional gene expression through antisense, or proto-oncogene activation.Here, we confirmed the upregulation of HML-6 in GBM patients. Future extensive bioinformatic studies with a tailored transcriptomic dataset of patient tumors will help clarify and validate the landscape of endogenous retroviruses in gliomas.Lastly, when running any systematic analyses for characterization of endogenous retroelement expression, a custom RNA-seq dataset is required. We typically recommend that RNA-seq datasets should be adequately prepared  with sufficient reading depth  to detect HERV sequences accurately. Although the TCGA dataset is not ideal for such analyses, we conducted a preliminary survival analysis to query the prognostic impact of HML-6 is overexpressed in a subset of highly invasive glioblastoma cell lines and patient-derived neurospheres. We have also demonstrated a potential survival detriment associated with elevated expression of the HML-6 product, ERVK3-1. Further analysis of ERVK3-1 is necessary to elucidate its role in glioma oncogenesis and recurrence. Our results have demonstrated several implications for the role of HML-6 and ERVK3-1 in the tumor biology of GBM as well as the potential clinical applications of future studies.In gliomas, HERV expression directly correlates with loss of DNA methylation at the corresponding HERV loci. Of differentially expressed HERVs, https://github.com/mlbendall/telescope_annotation_db). When final counts from the TSV file were retrieved, our initial HERV data set contained 2367, 1374, and 1243 unique loci for HERV expression in A172, M059J, H4, respectively38.Three glioma cell lines , M059J , H4 ) and control  were identified from whole-cell RNA-seq data from the ENCODE (ENCyclopedia Of DNA Elements) Consortium (encodeproject.org). Tier 1 sequence data was obtained from Sequence Read Archive (SRA) and aligned to the reference genome (hg38) using bowtie2. We utilized only paired-end ribosomal depleted samples from ENCODE with RNA fragments > 200 bp in our analyses. All cell lines originated from male patients; therefore, no gender-specific analysis was required. We ran the Telescope algorithm with default parameters on the resulting aligned BAM files using a previously published annotation of HERV locations (GTF)  and absolute FC > 1.2. Differentially expressed HERVs were noted in 39.9% (945/2367), 4.8% (66/1374) and 9.8% (122/1243) of all the unique HERV loci in the A172, M059J, and H4 cell lines, respectively. Differential gene expression (DEG) in each cell line were analyzed for overlapping and neighboring genes at dysregulated HERV loci using the R packages dplyr and Genomic Ranges.To characterize HERV differential expression, we utilized a previously published custom ensemble package in R, DESeq2, to conduct noise-modeling, data filtering, and statistical analysis of mean locus-specific HERV expression in the cancer cell lines relative to astrocyte controls. Differentially expressed HERVs (DE-HERVs) were considered significant if their expression crossed a minimum threshold of corrected 2FC > 1.2 and Benjamini–Hochberg corrected p < 0.05) and DE-HERV  coincided in differential expression , a DEG-HERV pair was noted, as previously described39.Among our differentially expressed HERV loci with differential methylation, we determined whether there were interceding antisense RNA sequences that may also result in differential HERV expression using RepeatMasker  on the opposing strand of interest. Proximity to the gene nearest to each HERV locus was calculated using the R package Genomic Ranges via the ‘DistanceToNearest’ function. KEGG.When a DEG  and then compared against the positions of our HERVs of interest. Finally, we identified genes that overlapped our Differentially Methylated Regions (DMR). A summary of our overall workflow is shown in Fig. To analyze methylation patterns in our cell lines, ‘.idat’ files from the ENCODE project were utilized. We created CSV files containing the basic metadata of our ‘.idat’ files and used the R packages minfi and illuminaio to read our ‘.idat’ files as methylation arrays. The resultant arrays were preprocessed to generate Methylation Sets (MSs) by normalizing the data, performing outlier assessment, and probe filtering. As in our HERV expression analysis, we employed quality control procedures to ensure that the analyzed methylation probes were ordered correctly and to remove any probes with SNPs at CpG islands. We also collected M (log2 fold ratio of methylated probes to unmethylated probes) and beta-values (percentage of methylated probes) before generating a model to assess methylation pattern by cell line. To assess differential methylation sites, we fit the model using Bayesian estimators and mapped the differentially methylated sites against the Illumina Human Methylation 450 K annotation. The locations of the differentially methylated loci were noted , supplemented with 20 ng/mL epidermal growth factor (EGF) and fibroblast growth factor (FGF), 2% B27 (Invitrogen), 1% penicillin–streptomycin and 1% sodium pyruvate (FisherScientific). TrypLE Express (Invitrogen) was used to dissociate neurospheres.A172 and Normal Human Astrocytes (NHA) were obtained from the American Type Culture Collection (ATCC) and maintained in recommended culture conditions: A172 (Dulbecco’s Modified Eagle Medium (DMEM) with 5% fetal bovine serum (FBS) supplemented with penicillin/streptomycin) and NHA . Non-adherent glioma patient-derived neurospheres  were obtained from the Mayo Clinic Brain Tumor Patient Derived Xenograft National ResourceHML-6). The HML-6 group is a member of the beta retrovirus superfamily (Class II) and includes several pro-viral loci and an intact open reading frame (ERVK3-1). The sequence for ERVK3-1 locus on Chromosome 19q13.43b was retrieved from dFam20 and University of California Santa Cruz Genome Browser using Repeat Masker21. Using primer3, two sets of primers for the HML-6 envelope locus ERVK3-1 were established (https://primer3.ut.ee) and validated for target specificity for the chromosome 19 locus using UCSC in-silico PCR (hg38)43.To validate our bioinformatics findings, we generated primers specific for the highest expressed HERV in our data set, Human endogenous Mouse mammary tumor (MMTV)-Like virus 6 (44. RNA was purified using DNase and quantified using NanoDrop 2000 (ThermoFisher Scientific) for a 260/280 value ~ 2.0, and reverse transcribed using the SuperScript First-Strand Synthesis RT-PCR kit (Thermo Fisher Scientific). Polymerase chain reactions were then used to amplify HML-6 transcripts on the cDNA using primers (10 uM) which target the protein-coding region of HML-6 (ERVK3-1). Fast SybR green master mix was used for the PCR with the following cycling conditions: 95 °C for 20 s,  repeated for 40 cycles followed by 95 °C for 20 s  for 40 cycles. Both primers targeted the env region of the HERVK3-1 downstream of LTR13. Ct values were normalized to expression of NHA using the delta-delta Ct method45. Reverse transcriptase negative controls were included and did not amplify.RNA was isolated from NHA, A172, GBM28, and GBM43 using Trizol extraction with chloroform as previously described PCR (UCSC Genome Browser) and Sanger Sequencing. Briefly, PCR products for both primer sets for ERVK3-1 were amplified using Q5 High-Fidelity DNA polymerase . Amplified product was run on a 1.8% agarose gel with GelStar Nucleic Acid Gel stain . A single DNA band was localized around 170–190 bp, excised and DNA was extracted using the QIAquick gel extraction kit  according to manufacturer’s protocol. The extracted DNA was amplified using GoTaq Polymerase . The amplified product was cloned using the TOPO TA Cloning kit for Sequencing with One Shot TOP10 Chemically Competent E. coli . Transformants were analyzed, grown overnight in 5 mL of LB and purified using QIAprep Spin Miniprep kit . Plasmid DNA was mixed with M13 Forward (− 20) Primer (5′-GTAAAACGACGGCCAG-3′) and sent for Sanger Sequencing to confirm the exact locus of HML-6 transcription in our samples. Sanger sequencing matched the PCR amplicon of ERVK3-1 for both primers. Results of sequencing are displayed in the Supplemental Material.To confirm amplicon sequence, our primers were validated using both in silicoHML-6 locus at chromosome 19q13.43b were established per manufacturer’s recommendation. The C2 probe for HML-6 was hybridized in the HybEZ Oven at 40 °C using Opal620 (red) fluorophores. Slides were co-stained with DAPI mounting media prior to microscopy. Bright-field images were acquired using a Leica SP8 confocal microscope . All images were analyzed using Imaris (Bitplane version 9.3). The presence of red punctate dots was considered positive for HML-6 transcripts. Negative controls were included per manufacturer protocol (ACD Bio). Sequence of the C2 probe is found in the Supplemental Material.Visualization of RNA transcripts was performed using RNA-scope Multiplex Assay v2  according to the manufacturer’s instructions. A172 cells were fixed using 4% paraformaldehyde on chambered slides, dehydrated, and rehydrated before probe hybridization. Probes specific for the Clinical information including ERVK3-1 expression was collected from The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) using the R package TCGABiolinks. Outliers were filtered using the R package survbootOutliers. We employed the OneStepDeletion (OSD) algorithm to maximize the concordance of the remaining data using a cox-regression model (c-index). Patient data was then further stratified by IDH mutation status and GBM subtype  for further analysis. Comparisons by GBM subtype were analyzed by multiple comparisons ANOVA with Tukey’s correction. Subsequently, TCGA findings were validated using data downloaded from the CGGA database, downloaded via GlioVis.Supplementary Information.


Purpose/Objective(s): Discovery of genetic drivers of radioresistance is critical for developing novel therapeutic strategies to combine with radiotherapy of radioresistant PDAC. In this study, we used genome-wide RNA-seq to identify genes upregulated in generated radioresistant PDAC cell lines and discovered the Inhibitor of DNA Binding 1 (ID1) gene as a potential regulator of radioresistance in PDAC.Materials/Methods: Radioresistant clones of the PDAC cell lines MIA PaCa-2 and PANC-1 were generated by delivering daily ionizing irradiation (IR) (2 Gy/day) in vitro over two weeks  followed by standard clonogenic assays following one week from the end of IR. The generated RR and parental cell lines were submitted for RNA-seq analysis to identify differentially expressed genes. The Limma R package was used to calculate differential expression among genes. Log2 fold change values were calculated for each sample compared to the control. Genes with an absolute fold change > 1 were considered significant. RNA sequencing expression data from the Cancer Genome Atlas (TCGA) database was analyzed through the online databases GEPIA, cBioPortal, and the Human Protein Atlas.Results: Following exposure to two weeks of 2 Gy daily IR in vitro, the two PDAC cell lines showed significantly greater clonogenic cell survival than their parental cell lines, indicating enhanced RR in these cells. RNA-seq analysis comparing parental and RR cell lines found upregulated seven genes  and seven genes downregulated  in both RR cell lines. Western blotting confirmed increased expression of the ID1 protein in the RR cell lines compared to their parental cell lines. We found that ID1 mRNA was significantly higher in PDAC tumors compared to matched normal and high ID1 expression correlated with significantly worse disease-free survival (DFS) in PDAC patients . ID1 mRNA expression was also strongly correlated in tumors with TP53 mutation, a known driver of radioresistance.Conclusion: Our analysis indicates a novel role of ID1 in PDAC radioresistance. ID1 expression is higher in tumor tissue compared to normal, and high expression correlates with both worse DFS and association with the TP53 mutation, suggesting that targeting ID1 prior to IR is an attractive strategy for overcoming radioresistance in PDAC. By the year 2040, the incidence of PDAC is expected to increase from 56,000 to 96,000 cases, a 66% increase. Pancreatic cancer is projected to become the second leading cause of cancer-related death, killing ~46,000 patients yearly in the U.S. alone. The only hope for long-term survival in patients with localized PDAC is surgical resection of the tumor. However, upwards of 60% of the patients with potentially curable localized PDAC do not present with frank eligibility for surgical resection, and therefore require an aggressive therapy regimen to reduce tumor burden enough to elicit removal. The standard course includes 3-6 months of multi-regimen genotoxic chemotherapies and, commonly, an additional 3-6 weeks of radiation therapy (RT). For most locally advanced PDAC patients, curative surgery will not be achieved despite these aggressive therapies due to the poor response of the tumor and thus remain unresectable. The rate of conversion from unresectable to resectable disease remains at a dismal ~15%. Unresected patients have nearly the same dismal survival rates as metastatic disease, with a prognosis of roughly 15-16 months and a poor quality of life. Why PDAC responds so poorly to typically effective strategies in other cancers is not entirely clear. It is speculated that the natural prevalence of robust DNA repair mechanisms in PDAC underlies resistance to genotoxic therapy. Therefore, understanding the biological underpinnings of radioresistant systems in PDAC is critical for uncovering vulnerabilities in the resistant phenotype.Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most lethal solid tumors with a rapid progression course and poor prognosis, contributing to a 10% of five-year survival rate for all stagesin vitro by treating them with daily doses of ionizing radiation and allowing the cells to recover, followed by an analysis of the changes in gene expression. Through next-generation RNA sequencing (RNA-seq), we discovered several novel genes differentially expressed after RR, including the inhibitor of DNA binding-1 (ID1). The ID1 gene has been previously shown to be upregulated in pancreatic cancer cells following nicotine stimulation via a Src kinase-dependent fashion leading to chemoresistance to gemcitabine treatment. Furthermore, elevated ID1 was shown to correlate with worse overall survival in resected pancreatic cancer patients. Another study in pancreatic cancer showed ID1 uncouples TGFβ-induced EMT from apoptosis leading to enhanced cell survival. The current study validates ID1 as a marker for radiation resistance in PDAC cells for the first time.In this study, we generated radiation-resistant (RR) PDAC cell lines 2 in DMEM media and supplemented with 10% fetal bovine serum  and penicillin/streptomycin .The human pancreatic adenocarcinoma cell lines MIA PaCa-2 and PANC1 were obtained from American Type Culture Collection  and maintained at 37 °C in 5% COCells were subjected to 2 Gray (Gy) radiation daily for 5 days, and then allowed to recover for two weeks, followed by a second course of radiation of 2 Gy daily for 5 days for a total of 20 Gy. Throughout the irradiation process and recovery time, cells were kept at 40%-70% confluency to ensure the potential for exponential growth. After completion of the second week of radiation, cells were collected for protein lysates, plated as single cells for clonogenic assays, or submitted for whole genome sequencing. Radioresistance of the cell lines was verified by comparing the radiosensitivity of the radiation-selected cells (after a recovery period) with their respective parental cell lines by the clonogenic assay as described below.Cell lysates were prepared using RIPA buffer  supplemented with 1x protease  and phosphatase inhibitors , followed by protein quantification by the Dc protein assay kit . Equal amounts of protein were loaded and resolved by SDS/PAGE and transferred to nitrocellulose membranes. The ID1 (Santa Cruz sc-133104) and GAPDH  primary antibodies were allowed to bind overnight at 4 °C and used at a dilution of 1:100-1000. After washing in TBS-Tween, membranes were incubated with StarBright Goat Anti-Mouse/Rabbit IgG secondary antibodies (Bio-Rad) diluted 1:2500-1:5000 for 1 h. Membranes were washed with TBS-Tween and then imaged on the ChemiDoc MP Imaging System (Bio-Rad).Cells were trypsinized to generate single cell suspensions and seeded onto 60 mm tissue culture plates in triplicate. Cells were then irradiated with various doses (0-8 Gy). Ten to 14 days after seeding, colonies were fixed with Methanol/Acetic Acid and stained with 0.5% crystal violet, and the numbers of colonies or colony forming units (CFU) containing at least 50 cells were counted using a dissecting microscope  and surviving fractions calculated. Experiments were repeated multiple, independent times.Irradiation was performed with the X-ray at 160 kV, 25 mA at a dose rate of approximately 113 cGy/min using an X-RAD 320 Biological Irradiator (Precision X-Ray Inc.).Total RNA was isolated with the TRIzol Reagent protocol  from the parental and RR cell lines. RNA quality and concentration were estimated with Bioanalyzer 2100 and RNA 6000 Nano Kit . RNA-Seq libraries were prepared with Illumina TruSeq Stranded mRNA LT Sample Preparation Kit  using 1 μg of total RNA according to the manufacturer’s protocol. Sequencing was performed by the genomics core at UAMS on the NovaSeq 6000 platform (Illumina).. Lowly expressed genes were filtered out and libraries were normalized by the trimmed mean of M-values. The Limma R package was used to calculate differential expression among genes. Log2 fold change values were calculated for each sample compared to the control. Genes with an absolute fold change > 1 were considered significant. RNA-seq reads were quality-checked, trimmed, and aligned to the hg38 reference genome (accession: GCA_000001405.15) using the Nextflow RNAseq pipeline, nf-core/rnaseq (version 3.4) available at DOI 10.5281/zenodo.1400710. The resulting gene counts were transformed to log2 counts per million (CPM)https://cbioportal.org/, http://gepia.cancer-pku.cn/, and https://www.proteinatlas.org/.The following websites were utilized to query for tumor and normal expression and clinical data for patients with PDAC in the TCGA: in vitro experiments, data are presented as the mean ± SEM for clonogenic survival. Statistical comparisons were made between the control and experimental conditions using the two-sided two-group t-tests with significance assessed at P < 0.05.For in vitro over five days, followed by a recovery time of 2 weeks followed by a second course of 10 Gy in 2 Gy per day fractions followed by an additional two weeks of recovery . We plotted mRNA expression against the IC50 value for 5-FU. We found ID1 expression had the highest correlation and strongest P-values with higher treatment resistance to 5-FU using Spearman and Pearson correlation tests. These results strengthen our findings that ID1 is a marker for resistance to DNA damaging agents in PDAC. Therefore, we further characterized the ID1 gene as a potential mediator of RR in PDAC patients.The full list of genes differentially expressed in both cell lines, function, mutational rate in PDAC, and prior studies examining radiation sensitivity is displayed in Following the generation of RR cell lines, we collected the protein lysates of the control and RR cell lines to test the expression of the ID1 protein. Western blotting revealed that ID1 protein expression was increased in the PANC-1 and MIA PACA-2 RR cells compared with parental PANC-1 and MIA PACA-2 cells  and B. Tvs. normal pancreas tissue showed significantly higher ID1 expression in the tumor samples compared to normal tissues (P < 0.05)   .. Patients with mutated p53 in multiple cancer types, including PDAC, have worse RT response rates than p53 wildtype patients. From our analysis of the TCGA PDAC cohort, p53 mutant patients had lower DFS than p53 wildtype patients (log rank P = 2.9e-3) [P = 0.03) [The p53 gene is a known regulator of cellular response to radiation therapy 2.9e-3) . We next = 0.03) .Together, our data showed that expression of ID1is upregulated in PDAC and is associated with poor survival and p53 mutation, supporting ID1 as a potential mediator in PDAC RR.-27. The aRT technique requires a highly specialized team of Radiation Oncologists and Physicists in addition to state-of-the-art technologies such as daily adaptive MR linear accelerators, only available in a handful of centers in the U.S.A. Outside of large tertiary cancer centers, delivery of aRT while safely avoiding normal tissue dosing is possible for a minority of PDAC patients where tumors are far enough away from the luminal gastrointestinal (GI) tract. Therefore, novel strategies to improve radiosensitivity to standard lower doses of RT are critical. To enhance radiosensitivity, genetic drivers of RR are crucial to be discovered to develop novel targeted therapeutics to combine with RT.Although the development of metastatic disease dominates the natural history of PDAC, local tumor progression contributes significantly to morbidity and mortality. Conventional RT (cRT) with small 1.8-2 Gy/fraction doses has shown low long-term tumor control rates or survival benefits. Over the past decade, dose-escalation strategies ), have shown promising resultset al. and Ogawa et al. used the cDNA microarray method to identify gene changes following radioresistance. The Soucheck study found the cholesterol pathway to be significantly upregulated in radioresistance cells, including genes: FDPS, ACAT2, AG2, CLDN7, DHCR7, ELFN2, FASN, SC4MOL, SIX6, SLC12A2, and SQLE. The Ogawa study found upregulated pathways related to growth factors, cell cycle checkpoint, and angiogenesis, including genes: AREG, MAPKAPK2, RGN, ANG-2. Our study generated RR PDAC cells and utilized the RNA-seq to further increase the current understanding of gene expression changes in radioresistant PDAC cell populations. The novelty of our study is that we used a clinically relevant dose fractionation of 2 Gy/day to generate our RR cell lines compared to a single 10 Gy dose in the Ogawa study. The Soucheck study also used this clinically relevant dose fractionation, but in contrast to our study, it only utilized one cell line. In this study, two different PDAC cell lines were generated, and genes that were upregulated in both were analyzed further, increasing the robustness of the current study.Two previous studies have studied gene regulation of radiation resistance using engineered radioresistance PDAC cell lines. Souchek et al. screened 38 PDAC cell lines and pulled out two radiation sensitive and two resistant cell lines to study the phosphoproteomes in response to 8 Gy of radiation. They found increased actin dynamics and FAK activity in the resistant cell lines and FAK inhibition radiosensitized radioresistant cell lines more than the radiosensitive cell lines. Schröter et al. examined the role of high dose radiation and changes in cell surface expression of immunomodulatory molecules. Interestingly, they found that only high radiation doses of > 5 Gy increased surface expression of PD-L1 and CD73. These results have implications for the potential use of immunotherapy combinations with radiation such as stereotactic body radiotherapy (SBRT) which utilizes high doses per fraction of radiation.Other studies have examined intrinsic radiation sensitivities in various PDAC cell lines to study potential therapeutic vulnerabilities. Wiechmann in vitro, and we discovered 14 genes that had significant changes in expression in the RR cell lines compared to the parental. Of these genes, we explored one gene, ID1, that has multiple prior published reports in other cancer types related to RR.In our study, we generated new RR PDAC cell lines and then further characterized the changes in gene expression using next-generation-based RNA-Seq. The new cell lines displayed higher survival levels after radiation . Maruyama et al. reported that PDAC cells expressed high levels of the ID genes and that ID1 expression was increased compared to normal controls. The authors concluded that increased ID1 expression might be associated with the enhanced proliferative potential of pancreatic cancer cells. In our analysis of the TCGA database, we found that ID1 expression was highly expressed in pancreatic tumor samples compared to normal pancreas tissue [ID1 is a helix-loop-helix (HLH) protein that can form heterodimers with members of the basic HLH family of transcription factors. The encoded protein has no DNA binding activity and, therefore, can inhibit the DNA binding and transcriptional activation ability of essential HLH proteins with which it interactss tissue .et al. showed that PGE2 signaling regulated radiation resistance in mouse glioblastoma (GBM) primary cultures in an ID1-dependent manner. In GBM, ID1 overexpression increased radioresistance, and ID1 expression levels predicted poor overall survival and knockdown of ID1 increased radiosensitivity in vitro. One of the other genes from our RNA-seq data shown to regulate radiation sensitivity was SPP1 in gastric adenocarcinoma.There have been several prior published publications examining the role of ID1 in radiation resistance, reviewed in .To explain the potential role of ID1 in associating with RR in PDAC cells, we found that patients with p53 mutation had significantly higher expression of ID1 mRNA than p53 wildtype patients. Qian and Chen reported that ID1 expression was downregulated following DNA damage in a p53-dependent manner. They found that ID1 overexpression promoted cell proliferation and inhibited DNA damage-induced cell senescence. They concluded that ID1 was a critical p53-dependent DNA damage response pathwayLooking into the future, ID1 may serve as a therapeutic target to increase the radiosensitivity of PDAC tumors and improve the outcomes in PDAC. Further studies developing strategies to target ID1 are warranted.


Vemurafenib‐induced drug resistance in melanoma has been linked to receptor tyrosine kinase (RTK) upregulation. The MITF and SOX10 genes play roles as master regulators of melanocyte and melanoma development. Here, we aimed to explore the complex mechanisms behind the MITF/SOX10‐controlled RTK‐induced drug resistance in melanoma. To achieve this, we used a number of molecular techniques, including melanoma patient data from TCGA, vemurafenib‐resistant melanoma cell lines, and knock‐down studies. The melanoma cell lines were classified as proliferative or invasive based upon their MITF/AXL expression activity. We measured the change of expression activity for MITF/SOX10 and their receptor (AXL/ERBB3) and ligand (NRG1/GAS6) targets known to be involved in RTK‐induced drug resistance after vemurafenib treatment. We find that melanoma cell lines characterized as proliferative (high MITF low AXL), transform into an invasive  cell state after vemurafenib resistance, indicating novel feedback loops and advanced compensatory regulation mechanisms between the master regulators, receptors, and ligands involved in vemurafenib‐induced resistance. Together, our data disclose fine‐tuned mechanisms involved in RTK‐facilitated vemurafenib resistance that will be challenging to overcome by using single drug targeting strategies against melanoma. Besides immunotherapy, small molecule inhibitors selectively targeting the BRAF kinase such as vemurafenib, dabrafenib, and encorafenib have been successful in treating BRAF mutant melanoma. However, reactivation of the MAPK pathway as well as an increase in PI3K signaling is a major challenge for therapy response, due to both intrinsic and acquired resistance.In response to MAPK pathway inhibition, melanoma cells may undergo a transcriptional reprogramming event, where proliferative melanoma cells switch into a phenotypically distinct invasive cell population.We therefore set out to investigate the role of the SOX10/MITF axis upon RTK regulation during the development of vemurafenib resistance. In a panel of melanoma cell lines, we monitored gene expression levels of ligands and receptors previously linked to the SOX10/MITF axis. We identified SOX10, MITF, ERBB3, and GAS6 as upregulated markers during early vemurafenib treatment, while EGFR, AXL, and NRG1 were upregulated after the establishment of vemurafenib resistance. Interestingly, we also found evidence for AXL/ERBB3 receptor redundancy, further demonstrating the challenges of various treatment strategies. Our study opens up for further elucidation of transcriptional reprogramming events during MAPK pathway inhibition that may contribute to stratifying the melanomas and serve as a tool for appropriate therapy selection.2We set out to investigate whether the MITF/SOX10 axis affects ERBB3 and AXL receptors throughout melanoma progression and treatment, as RTKs are frequently identified as targets of negative feedback loops and involved in resistance mechanisms in melanoma.2.1R = −0.55, p = <.001), which is in agreement with results from previously published studies, in that melanomas lacking MITF exhibit high levels of AXL.R = −0.3, p = <.001), and finally, a moderate inverse correlation was found between AXL and ERBB3 in patient samples   to investigate correlations between MITF/SOX10, SOX10/ERBB3, and MITF/NRG1.) Figure .2.2To investigate whether melanoma cell lines recapitulated the findings from the patient samples, we examined gene expression levels in a microarray format on a melanoma panel consisting of 17 cell lines spanning various mutational backgrounds and tumor stages. These cell lines represent the primary tumors WM115 (BRAFV600E), WM35 (BRAFV600E), WM1341 (BRAFV600E) and WM1366 (NRAS), and the metastatic cell lines A375 (BRAFV600E), WM9 (BRAFV600E), SKMEL28 (BRAFV600E), WM45.1 (BRAFV600E), WM983B (BRAFV600E), WM266.4 (BRAFV600E), WM239 (BRAFV600E), MeWo (NF1), FEMXI (HRAS), FEMXV (HRAS), WM1341 (triple wild type), and WM852 (NRAS). Clustering of the expression levels across the genes of interest appeared to be in agreement with the TCGA analysis, in that the cell lines A375, WM9, WM852, WM1366, and LOXIMVI show a clear inverse correlation between AXL and MITF, displaying low expression levels of MITF and high expression levels of AXL. WM852, WM1366, and LOX also display low expression levels of ERBB3 and SOX10, while A375 and WM9 show medium expression levels. FEMXI, FEMXV, 45.1, WM115, WM239, WM35, WM1341 and SKMEL28 cell lines display a clear inverse correlation, with high levels of MITF, SOX10 and ERBB3 and low levels of AXL. Finally, the MeWo, WM983B, and WM266.4 cell lines show high expression of all the genes of interest. Based on these findings, we classified our cell lines into three different groups based on their expression levels of AXL and ERBB3. The distinct population of high AXL expression and low MITF/SOX10/ERBB3 suggests that these cells are of the invasive phenotype, as previously proposed by Verfaille et al.To further validate the results from the TCGA and gene expression data, we measured the basal RNA expression levels of NRG1, AXL, and GAS6, as well as utilizing our previously published values for SOX10, MITF, ERBB3.In an attempt to further elucidate the interplay between MITF/SOX10 and ERBB3/AXL, we depleted SOX10 and MITF alone or in combination in the WM115 cell line, this cell line contains high levels of MITF, SOX10, and ERBB3, and low AXL expression. We found that MITF depletion alone increased both RNA and protein levels of AXL, ERBB3 and SOX10. Moreover, we found reduced RNA and protein levels of both MITF and ERBB3 following SOX10 depletion. AXL levels were not elevated following SOX10 depletion, while it did result in reduced MITF levels. This was surprising, as in theory, increased levels of AXL would be expected. Whether this is a general mechanism or exclusive to this cell line is currently unknown, and should be further explored. Depleting the combination of MITF and SOX10 demonstrated the expected downregulation of ERBB3,2.3Our cell lines displayed two distinct phenotypic groups characterized by either high expression of AXL or high expression of ERBB3. We therefore wanted to investigate the possibility of receptor redundancy between ERBB3 and AXL. Five cell lines containing different mutational backgrounds were selected, WM1341, WM9, and WM983 represented BRAFV600E mutants, while MeWo represented NF1 mutations, and FEMXI represented HRAS‐mutant melanoma. We found that downregulation of AXL expression led to an increase in ERBB3 levels at both the RNA and protein levels in all cell lines tested. Interestingly, when knocking down AXL in the MeWo and WM983B cell lines, we observed an up‐regulation of ERBB3, indicating an apparent two‐way redundancy Figure . This isAs both AXL and ERBB3 have been implicated in vemurafenib resistance and PI3K pathway activation we wanted to investigate whether the levels of receptors dictate this pathway activation, and whether signaling persists exclusively through one receptor, and not the other. We selected the two cell lines WM852 (AXL high/ERBB3 low) and WM115 (ERBB3 high/AXL low), representing opposite expression levels. We then treated WM852 with the AXL inhibitor R428 (BGB324/bemcentinib), and WM115 with the ERBB inhibitor sapatinib (AZD8931) for 72 h. 30 and 15 min prior to harvest, we added both the AXL ligand GAS6 and the ERBB3 ligand NRG1 to both cell lines, respectively. Western blots show that the addition of GAS6 activated pAKT in WM852, but not in WM115. By contrast, the addition of NRG1 activated pAKT in both cell lines, although just slightly in WM852. The reason for this might be that although the levels are low, ERBB3 is still expressed at low levels in the WM852 cell line. The addition of GAS6 to the R428‐treated cells did not lead to activation of pAKT, demonstrating the efficacy of the inhibitor in this cell line. Moreover, the addition of NRG1 to the sapatinib‐treated WM115 cells reduced pAKT activation significantly as compared with control Figure .We next asked whether this AXL high, MITF/SOX10/ERBB3 low, and NRAS‐mutated cell line could be treated with AXL inhibitor monotherapy. Using MTS and IncuCyte measures as readout, we found that the WM852 cell line stopped proliferating at relatively low doses (1 and 2 μM), suggesting that the AXL inhibitor R428 alone may abrogate growth in these cells Figure . These r2.4To further investigate the consequences of AXL and ERBB3 receptor redundancy, we wanted to study the dynamics of the receptors in our cell lines in relation to the acquisition of resistance against BRAF inhibition. We treated the BRAFV600E mutated melanoma cell lines WM983, WM239, SKMEL28, WM9, and A375 with an increasing dose of the BRAF inhibitor vemurafenib. We considered the cells resistant when they proliferated at a 3 μM vemurafenib concentration, and we collected mRNA after 72 h, 1 week, and after the acquisition of resistance. RT‐PCR results showed MITF and SOX10 upregulation at 72 h and 1 week following treatment in all five cell lines, with a loss of both MITF and SOX10 expression after resistance had been attained. Moreover, we found that ERBB3 was upregulated at both 72 h and 1 week following vemurafenib treatment in WM983B, WM239, WM9, and A375, in agreement with previously published work.Interestingly, Western blots of the AXL high/MITF low cell line A375 show a small pAKT increase, reduced AXL levels, and an extensive pERK level increase, suggesting that the A375 cell line mainly obtains resistance through increased MAPK pathway signaling. By contrast, the AXL low cell line SKMEL28 displays unchanged pERK levels, increased AXL levels, and increased pAKT levels, implying PI3K‐induced resistance Figure .ClinicalTrials.gov; NCT02872259). However, it is important to consider the tumor heterogeneity, as well as the complexity of the melanoma signaling network. Receptor redundancy and pathway crosstalk is a major hindrance that complicates treatment tremendously.Adaptive cellular behavior involving RTK upregulation in response to MAPK treatment is of great interest, and patient trials combining MAPK inhibitors with RTK inhibitors are ongoing .Melanoma cell lines, WM35, WM1341, WM115, WM1366, WM45.1, WM266.4, WM983B, WM852, and WM9 cell lines were obtained from the Wistar Institute, SKMEL28, MeWo, and A375 were obtained from the American Type Culture Collection , and LOXIMVI established in‐house. The immortalized melanocyte cell line Hermes 3C was purchased from the Wellcome Trust Functional Genomic cell bank.4.2Cells were seeded on six‐well plates and grown to 60% confluence. Cells were then transfected by RNA silencing and incubated for 72 h using siRNA directed against MITF‐M and SOX10 , ERBB3 (Life Technologies), and AXL Silencer® Select (S1845) (Thermo Fisher Scientific). After testing several time points a 72 h incubation time was chosen to ensure an optimal reduction of our targets at the protein level. The cells were transfected with a final concentration of 25 pmol siRNA using Lipofectamine RNAiMAX (Invitrogen) as described in the manufacturer's protocol. Sequences for siRNA can be found in the Supplementary Methods. All siRNA transfections were performed in triplicate.4.3Melanoma protein cell lysates were separated using SDS page using 4%–12% NuPAGE® Novex Bis‐Tris Midi‐ Gels , and then transferred to a nitrocellulose membrane using iBot2 dry blotting system . The membranes were then blocked using 5% BSA for 1 h, before incubation with primary antibody at 4°C overnight. To remove residual primary antibodies the membranes were washed for 3 × 10 min in TBS/T . Next, a horseradish peroxidase conjugated secondary antibody was applied for 1 h at room temperature . The protein bands were visualized by chemiluminescence using SuperSignal™ West Dura Extended Duration substrate . Protein bands were visualized using the Bio‐Rad ChemiDoc™ imaging system (Bio‐Rad), and images were prepared using Adobe Photoshop CC 2017 .4.4Antibodies were purchased from Cell Signaling Technology ; MITF , Her3/ERBB3 XP , AXL (1:1000 #8661), SOX10 , Phospho‐Akt‐Serine‐473 XP® , Phospho‐p44/42 (ERK1/2) XP®  and Histone H3  was used as a loading control. Secondary antibody against rabbit  was purchased from Dako . The small molecular inhibitors vemurafenib (Plexxikon 4032), AZD8931 (Sapatinib) and R428 (BGB324/bemcentinib) were obtained from Selleck Chemicals . Recombinant human NRG1‐beta protein‐ligand was purchased from ImmunoTools GmbH . Recombinant human GAS6 protein‐ligand was purchased from R&D Systems .4.5Total RNA was isolated from the melanoma cells using the GenElute Mammalian Total RNA Miniprep Kit . For reverse transcription, the qScript™ cDNA Synthesis Kit  was used. Both kits were used according to the manufacturer's manuals. Total RNA was then measured using NANODROP 2000 (Thermo Scientific).4.6The SYBR Green system was used for Real‐time detection. 30 μl PerfeCTa™ SYBR® Green SuperMix for iQ , 100 ng cDNA, 300 nM of each primer and nuclease‐free water were added to a final volume of 60 μl was used for each PCR reaction. The final volume of 60 μl was then split into two parallels of 25 μl and added to the PCR plate. Primers against MITF‐M, ERBB3, SOX10, AXL, GAS6, and NRG1 were ordered from Integrated DNA Technologies (IDT). Real‐time reactions were run on a CFX Connect Real‐Time PCR Detection System (Bio‐Rad) with the protocol previously described.4.7ueous One Solution  according to the manufacturer's protocol.Cells were seeded in six‐well plates using approximately 120 000 cells per well. Cell proliferation was monitored using IncuCyte® . The IncuCyte system estimates the area of the well occupied by attached cells. The viability of the cells was then measured by 3‐‐5‐(3‐carboxymethoxy phenyl)‐2‐(4‐sulfophenyl)‐2H‐tetrazolium (MTS) using CellTiter 96® AQ4.8t‐test in GraphPad Prism 6. p < .05 were considered statistically significant. Experiments were performed in three biological replicates. For the construction of the heatmap logarithmized values (matrix)(log2 + 1) were used. Spearman's correlation was applied to calculate the correlation between MITF, ERBB3, SOX10, AXL, GAS6, and NRG1, respectively.Statistical significance of differences between control group and RTK levels was performed using student's 4.9https://xenabrowser.net/) (n = 472).TCGA Melanoma (TCGA‐SKCM) gene expression RNAseq files were extracted from UCSC Xena . RNA quality was assessed using the Agilent 2100 Bioanalyzer  and mRNA expression profiling was performed using the Illumina HumanHT‐12 v4 Expression BeadChip according to the manufacturer's protocol. Extraction of the data and quality control of the raw data was performed using Illumina's Genome studio software V2011.1. Heatmaps and clustering were performed in R.Karen‐Marie Heintz: Data curation (supporting). Eivind Hovig: Funding acquisition (lead); project administration ; supervision ; writing – review and editing . Sigurd L. Bøe: Conceptualization ; investigation ; project administration (lead); supervision (lead); writing – review and editing .The authors declare that they have no competing interests.Not applicable.APPENDIX S1: Supporting InformationClick here for additional data file.


DLST) is associated with poor outcome in neuroblastoma patients and triple-negative breast cancer (TNBC) and specifically with the oxidative phosphorylation (OXPHOS) pathway. Inhibitors of OXPHOS were previously suggested as a potential therapeutic strategy for a subset of patients with high-risk neuroblastoma. Here, we tested if cell lines with DLST amplifications or high mRNA levels were associated with sensitivity to 250 drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset by comparing them to cell lines without these changes. DLST-altered cell lines were more sensitive to 7 approved drugs, among these obatoclax mesylate, a BCL2 inhibitor that reduces OXPHOS in human leukemia stem cells. Moreover, several protein kinase inhibitors were identified to be efficient in cell lines with DLST amplifications or high mRNA levels, suggesting a vulnerability of DLST-altered cell lines for drugs targeting the ERK/MAPK pathway. Furthermore, increased DLST expression in cell lines with driver mutations in KRAS supported this relationship. We therefore conclude that, in addition to OXPHOS, protein kinases could be potential targets of therapy in the presence of DLST amplifications or high mRNA levels. The new drug candidates proposed here could serve in experimental testing on drug efficacy in knock-in cell lines and DLST-activated tumors.Overexpression of the dihydrolipoamide S-succinyltransferase ( DLST) gene has been shown to predict poor outcome of neuroblastoma and triple-negative breast cancer (TNBC) [DLST led to apoptosis in T-cell acute lymphoblastic leukemia and in TNBC [DLST dependence for tumor cell viability and, consequently highlighting DLST as a potential target for cancer therapy. With respect to its cellular function, DLST converts α-ketoglutarate to succinyl-CoA during oxidative decarboxylation and produces NADH for the oxidative phosphorylation (OXPHOS). Specifically, DLST is an E2 subcomponent of the mitochondrial oxoglutarate dehydrogenase complex (OGDC), catalyzing the overall conversion of 2-oxoglutarate to succinyl-CoA during the tricarboxylic acid (TCA) cycle. There is evidence that certain cancer cells use the TCA cycle extensively for energy production, offering potential therapeutic opportunities by targeting the TCA cycle in cancer cells [DLST-overexpressing cell lines showed increased OXPHOS gene signatures [in vivo [DLST-activated tumors within and beyond OXPHOS inhibition and drug repurposing are conceivable.Recently, an increased expression of the dihydrolipoamide S-succinyltransferase (r (TNBC) , 2. In c in TNBC , 3 suggeer cells . In line[in vivo . Thus, aDLST-activated tumors. With the advent of complex genetic datasets of roughly 1000 cell lines in the Cancer Cell Line Encyclopedia (CCLE) and on drug resistance in the Genomics of Drug Sensitivity in Cancer project (GDSC), analyses of drug sensitivity have become possible on a larger scale [DLST activation, caused by overexpression or copy number amplification, is associated with drug sensitivity and whether additional OXPHOS inhibitors may serve as therapeutics for tumors with high levels of DLST.Therefore, we set out to identify new candidates for the treatment of er scale , 7. We tDLST-activated tumor cells, we compared cell lines with supposedly activating changes of DLST  to cell lines without DLST changes. In the light of the described DLST activation in neuroblastoma [DLST copy number and none of them showed high mRNA levels, hence, a test of nervous system tumors was not possible (Supplementary Table 1).To identify drugs that inhibit viability of specifically blastoma , we aimeDLST. We tested resistance and sensitivity for the full set of 250 individual drugs and found that DLST-activated cell lines were more often sensitive to 7 drugs compared to control cell lines without such changes  and in vitro   cell lines showed these supposedly activating genetic alterations in S708163) . NeuroblDLST-activated cell lines would be associated with resistance in DLST-deactivated cell lines . This assumption was true for lapatinib, CP724714, and obatoclax mesylate, which showed an OR for resistance >1 in cell lines with DLST deactivation, however, lacking statistical significance (Supplementary Table 4).We hypothesized that in an inverse scenario of the described associations above, drugs associated with sensitivity in DLST-activated cell lines to MAPK inhibitors could be due to concomitant changes in the ERK/MAPK pathway. Such common driver mutations occur in NRAS, KRAS and HRAS. However, DLST-activated cell lines did not show alterations in any of the RAS genes more frequently than all other samples . We, therefore, investigated whether DLST expression was associated with mutation or expression of common genes involved in the ERK/MAPK pathway. DLST expression was not associated with driver mutations in BRAFV600 (p = 0.856), HRAS (p = 0.791) or NRAS (p = 0.609) . However, cell lines with driver mutations in KRAS had higher DLST expression than controls , HRAS , KRAS  was observed.As stated above, the primary analyses identified only one OXPHOS inhibitor but several protein kinase inhibitors were associated with sensitivity. Because this points to an involvement of DLST in the ERK/MAPK pathway, we asked if the sensitivity of DLST amplification or high mRNA levels are rather rare in cell lines derived from the nervous system, they may nevertheless have an impact on cancer growth as DLST overexpression had led to increased tumor burden and higher frequency of disseminated disease in a zebrafish model of MYCN-driven neuroblastoma [DLST expression was associated with poor overall survival also in triple-negative breast cancer patients [DLST activation in further cancer entities.New therapy options are still being explored for the treatment of neuroblastoma including potential molecular pathways and genetic aberrations. While our results indicate that blastoma . Moreovepatients  and may DLST in cancer was associated with aggressiveness in neuroblastoma and with the OXPHOS pathway specifically. Studies showed that OXPHOS can be upregulated to support the growth, survival, and migration of cancer cells, and upregulated DLST expression may pose a mechanism to increase OXPHOS [DLST-altered tumors. Three OXPHOS inhibitors  were suggested to inhibit growth of neuroblastoma cells and partly induced apoptosis in zebrafish and mouse xenografts of high-risk neuroblastoma [DLST-activated tumors with elevated OXPHOS, it is of particular interest that in our analysis of a large number of cancer cell lines, DLST-activated cell lines were sensitive to obatoclax mesylate more often than control cell lines. This inhibitor of the Bcl-2-family proteins has previously shown to reduce OXPHOS in human leukemia stem cells [DLST-activating alterations and should be further investigated in this context [Recently, the elevated expression of e OXPHOS , 5. Givee OXPHOS , OXPHOS blastoma . In the em cells . MoreoveDLST in OXPHOS and the identified OXPHOS inhibitor, we found a group of protein kinase inhibitors potentially suitable for therapy of such tumors . We integrated these genetic data with drug resistance data on 250 drugs of the Genomics of Drug Sensitivity in Cancer project GDSC [https://tools.hornlab.org/GDSC/ [DLST defined by copy number amplification or high mRNA expression of DLST (Supplementary Tables 1–3) because, as part of the mitochondrial oxoglutarate dehydrogenase complex (OGDC) and rate limiting enzyme of the TCA cycle, we assumed DLST levels to have direct impact on OGDC activity. We then also analyzed supposedly deactivating changes defined as homozygous deletion, low mRNA, frameshift or nonsense variants . Low and high mRNA expression levels were defined with a minimum z-score of -2 and 2, respectively, based on RNA-seq RPKM units compared to the expression distribution of diploid samples in cBioPortal [DLST-activated) and the control cell line group without any DLST changes (DLST wild types) (two-sided Fisher’s exact and chi-square tests). Bonferroni corrected p-value thresholds were < 0.0023 testing 22 pathways (each pathway containing pooled sets of drugs assigned to this pathway) and p < 0.0002 testing 250 individual drugs.To test differences in drug sensitivity in rg/GDSC/ , resulteioPortal . To deten = 54) or from neuroblastoma . We further tested DLST expression in cell lines across entities (CCLE 2019 cohort) with BRAFV600, NRAS, KRAS, and HRAS driver mutations and tested co-expression with genes involved in the ERK/MAPK pathway .Initially, we analyzed all cell lines across entities and then specifically a subgroup of 70 cell lines that had been documented to derive from the central nervous system (