Mutant KRAS associated malic enzyme 1 expression is a predictive marker for radiation therapy response in non-small cell lung cancer
© Chakrabarti. 2015
Received: 22 April 2015
Accepted: 6 July 2015
Published: 16 July 2015
Advanced non-small cell lung cancer (NSCLC) is an aggressive tumor that is treated with a combination of chemotherapy and radiation if the patient is not a candidate for surgery. Predictive biomarkers for response to radiotherapy are lacking in this patient population, making it a non-tailored therapy regimen with unknown outcome. Twenty to 30 % of NSCLC harbor an activating mutation in KRAS that may confer radioresistance. We hypothesized that mutant KRAS can regulate glutamine metabolism genes in NSCLC and maintain tumor redox balance through transamination reactions that generate cytosolic NADPH via malic enzyme 1 (ME1), which may contribute to radioresistance.
A doxycycline-inducible mouse model of KRAS G12D driven NSCLC and patient data was analyzed from multiple publicly accessible databases including TCGA, CCLE, NCBI GEO and Project Achilles. ME1 expression was found to be mutant KRAS associated in both a NSCLC mouse model and human NSCLC cancer cell lines. Perturbing glutamine metabolism sensitized mutant KRAS, but not wild-type KRAS NSCLC cell lines to radiation treatment. NSCLC survival analysis revealed that patients with elevated ME1 and GOT1 expression had significantly worse outcomes after radiotherapy, but this was not seen after chemotherapy alone.
KRAS driven glutamine metabolism genes, specifically ME1 and GOT1 reactions, may be a predictive marker and potential therapeutic target for radiotherapy in NSCLC.
Background and findings
Mutant KRAS is associated with ME1 and GOT1 expression in NSCLC
GSEA results for mutant vs. wild-type KRAS NSCLC cell lines
GOT1, GOT2, GLS
ME1, ME3, GOT1, GOT2, MDH1
Next, we utilized gene expression data (GSE40606) of a tetracycline operator-regulated Tet-op-KRAS G12D ; p53 −/− transgenic mouse model of NSCLC to examine mRNA expression in the KRAS induced (“ON”) and extinguished (“OFF”) states (Fig. 1b). When fed doxycycline, the mice develop lung tumors that are dependent on constitutive KRAS G12D expression . Within 48 h of doxycycline withdrawal, KRAS G12D expression was extinguished and whole-genome gene expression analyses of lung tumors were performed. Consistent with our cell line results, ME1 and GOT1 levels were significantly upregulated when KRAS G12D (n = 4 mice) was induced vs 48 h extinction with doxycycline withdrawal (n = 4 mice) (Fig. 1b). We found that KRAS G12D induction similarly upregulated ME1 and GOT1 mRNA in mouse doxycycline inducible KRAS G12D embryonic fibroblasts derived from the transgenic mice (Fig. 1c, Additional file 1: Figure S1B).
Next, we measured mRNA levels of ME1 and GOT1 in 11 mutant and 11 wild-type KRAS NSCLC cell lines and found both genes to be significantly upregulated in the mutant cell lines (Fig. 1d, Additional file 1: Figure S1C). Next, to determine if mutant KRAS NSCLC cell lines relied on ME1 for survival, we analyzed 17 NSCLC cell lines from the Project Achilles database, an openly accessible platform of large-scale functional RNAi screens of cancer cell lines to identify genes that affect cell survival . We found that 7 out of 9 mutant KRAS cell lines relied on ME1 for viability, while ME1 was dispensable in all but one of the wild-type cell lines (Fig. 1e). To verify these results, we knocked down ME1 (Fig. 1h) in H522, a wild-type KRAS line, and in HCC44, a mutant KRAS line. Using clonogenic survival assays, we found that ME1 loss rendered HCC44, but not H522, unable to form visible colonies (Fig. 1f, g). Taken together, our analyses indicate that mutant KRAS is associated with ME1 gene expression in NSCLC and that ME1 is an essential viability gene in mutant, but not wild-type, KRAS cell lines. In support of this observation, ME1 is a known NRF2 transcriptional target, which itself is positively regulated by mutant KRAS signaling via the MAPK pathway [22, 23].
Targeting glutamine metabolism sensitizes mutant KRAS NSCLC cell lines to radiation treatment
GOT1 and ME1 expression predicts response to radiation therapy in NSCLC patients
This multi-database translational study is the first to identify mutant KRAS associated glutamine metabolism genes, GOT1 and ME1, as potential radioresistance biomarkers in NSCLC. Our study revealed that elevated expression of GOT1 or ME1 is a highly predictive biomarker in radiation treatment, but not chemotherapeutic, outcomes. Additionally, ~93 % of patients with a complete response to IR treatment harbored wild-type KRAS in their tumors. To explain these observations, we hypothesize that KRAS-reprogrammed glutamine flux through GOT1 and ME1 is critical to maintain cytosolic NADPH levels for redox balance and lipid synthesis in NSCLC. In the face of ROS stress, as observed with IR treatment, NADPH is preferentially used to maintain reduced glutathione and thioredoxin 1 to protect cells from ROS damage . In this context, KRAS may reprogram NSCLC glutamine metabolism similar to that observed in pancreatic cancer to maintain redox balance, thus providing an oncogene driven mechanism of radioresistance. While there are currently no known specific inhibitors of ME1 or GOT1, targeting upstream glutamine utilization via glutaminase 1 (GLS1, Fig. 1a) inhibition (with BPTES or CB-839) may blunt downstream utilization of glutamine/glutamate through GOT1 and ME1, thus depleting tumor, but not normal tissue, NADPH/GSH production, leading to tumor-specific radiosensitivity while sparing normal tissue .
Materials and methods
GSEA of mutant vs wild-type KRAS NSCLC cell lines was completed using the Broad Institute’s publically available Cancer Cell Line Encyclopedia (CCLE) (http://www.broadinstitute.org/ccle) . Transgenic mouse data was obtained through GEO Series accession number GSE40606 at Transgenic mouse data was obtained through GEO Series accession number GSE40606. We obtained NSCLC expression, mutation, treatment and outcomes patient data from The Cancer Genome Atlas (TCGA) using the lung adenocarcinoma (LUAD) dataset (https://tcga-data.nci.nih.gov/tcga/tcgaCancerDetails.jsp?diseaseType=LUAD&diseaseName=Lung adenocarcinoma) . Level 2, tumor somatic mutation data was obtained for KRAS for each patient in the analysis (Fig. 3a). Level 2, normalized gene expression data was obtained for GOT1 and ME1 for each patient in the analysis (Fig. 3b, c). Patient characteristics are shown in Additional file 2: Table S1 and Additional file 3: Table S2. Cell line gene dependency data was obtained from Broad Institute’s Project Achilles (http://www.broadinstitute.org/achilles) .
Survival analysis in radiation treated NSCLC patients (n = 73) was conducted using the Kaplan-Meier Plotter webtool (kmplot.com) . Briefly, kmplot segregates each gene into percentile of expression between the lower and upper quartiles and the best performing threshold is used as the final cutoff in a univariate Cox regression analysis. Kaplan-Meier survival plot and the hazard ratio with 95 % confidence intervals and logrank P value is calculated with the Bioconductor package in R.
Ethical approval and consent
All human data is sourced through The Cancer Genome Atlas (http://cancergenome.nih.gov/), no patients were approached for this study. No consent and no ethical approval were required to utilize this database.
For clonogenic survival assays, cells were trypsinized and plated onto 6-well plates at 100, 500, or 1000 cells per well in 2 ml of complete media, Gln deprived media for 16 h or complete media containing 1 μM CB-839 for 48 h. Cells were then exposed to IR (at various doses as indicated), allowed to grow for 7 days, washed with PBS and stained with crystal violet solution. Colonies with >50 normal appearing cells were counted and percent survival calculated and graphed with dose.
For siRNA transfection, cells were plated in 10 cm plates at 2 × 105 cells per plate and transfected with either control siRNA or siRNA against ME1 for 48 h followed by clonogenic survival assay.
The author thanks Dr. David A. Boothman for his salary support within the Simmons Comprehensive Cancer Center, which was funded by NIH/NCI grant 5P30CA142543.
- Bi N, Wang L. Superiority of Concomitant Chemoradiation Over Sequential Chemoradiation in Inoperable, Locally Advanced Non-Small Cell Lung Cancer: Challenges in the Selection of Appropriate Chemotherapy. Semin Radiat Oncol. 2015;25(2):122–32.PubMedView ArticleGoogle Scholar
- Rowell NP, Williams CJ. Radical radiotherapy for stage I/II non-small cell lung cancer in patients not sufficiently fit for or declining surgery (medically inoperable). Cochrane Database Syst Rev. 2001;1:CD002935.PubMedGoogle Scholar
- Warram J. Preoperative irradiation of cancer of the lung: final report of a therapeutic trial. A collaborative study. Cancer. 1975;36(3):914–25.PubMedView ArticleGoogle Scholar
- Dilling TJ. Radiation dose in non-small cell lung cancer: too much of a good thing? Int J Radiat Oncol Biol Phys. 2014;90(5):979–82.PubMedView ArticleGoogle Scholar
- Westcott PM, Halliwill KD, To MD, Rashid M, Rust AG, Keane TM, et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature. 2015;517(7535):489–92.PubMedView ArticleGoogle Scholar
- Mak RH, Hermann G, Lewis JH, Aerts HJ, Baldini EH, Chen AB, et al. Outcomes by tumor histology and KRAS mutation status after lung stereotactic body radiation therapy for early-stage non-small-cell lung cancer. Clin Lung Cancer. 2015;16(1):24–32.PubMed CentralPubMedView ArticleGoogle Scholar
- Bernhard EJ, Stanbridge EJ, Gupta S, Gupta AK, Soto D, Bakanauskas VJ, et al. Direct evidence for the contribution of activated N-ras and K-ras oncogenes to increased intrinsic radiation resistance in human tumor cell lines. Cancer Res. 2000;60(23):6597–600.PubMedGoogle Scholar
- Affolter A, Drigotas M, Fruth K, Schmidtmann I, Brochhausen C, Mann WJ, et al. Increased radioresistance via G12S K-Ras by compensatory upregulation of MAPK and PI3K pathways in epithelial cancer. Head Neck. 2013;35(2):220–8.PubMedView ArticleGoogle Scholar
- Minjgee M, Toulany M, Kehlbach R, Giehl K, Rodemann HP. K-RAS(V12) induces autocrine production of EGFR ligands and mediates radioresistance through EGFR-dependent Akt signaling and activation of DNA-PKcs. Int J Radiat Oncol Biol Phys. 2011;81(5):1506–14.PubMedView ArticleGoogle Scholar
- Xu D, Allsop SA, Witherspoon SM, Snider JL, Yeh JJ, Fiordalisi JJ, et al. The oncogenic kinase Pim-1 is modulated by K-Ras signaling and mediates transformed growth and radioresistance in human pancreatic ductal adenocarcinoma cells. Carcinogenesis. 2011;32(4):488–95.PubMed CentralPubMedView ArticleGoogle Scholar
- Toulany M, Dittmann K, Kruger M, Baumann M, Rodemann HP. Radioresistance of K-Ras mutated human tumor cells is mediated through EGFR-dependent activation of PI3K-AKT pathway. Radiother Oncol: J of the European Soc for Therapeutic Radiology and Oncology. 2005;76(2):143–50.View ArticleGoogle Scholar
- Caron RW, Yacoub A, Zhu X, Mitchell C, Han SI, Sasazuki T, et al. H-RAS V12-induced radioresistance in HCT116 colon carcinoma cells is heregulin dependent. Mol Cancer Ther. 2005;4(2):243–55.PubMedGoogle Scholar
- Kamphorst JJ, Nofal M, Commisso C, Hackett SR, Lu W, Grabocka E, et al. Human pancreatic cancer tumors are nutrient poor and tumor cells actively scavenge extracellular protein. Cancer Res. 2015;75(3):544–53.PubMedView ArticleGoogle Scholar
- Brunelli L, Caiola E, Marabese M, Broggini M, Pastorelli R. Capturing the metabolomic diversity of KRAS mutants in non-small-cell lung cancer cells. Oncotarget. 2014;5(13):4722–31.PubMed CentralPubMedGoogle Scholar
- Saqcena M, Mukhopadhyay S, Hosny C, Alhamed A, Chatterjee A, Foster DA. Blocking anaplerotic entry of glutamine into the TCA cycle sensitizes K-Ras mutant cancer cells to cytotoxic drugs. Oncogene. 2014;34(20):2672–80.PubMedView ArticleGoogle Scholar
- Son J, Lyssiotis CA, Ying H, Wang X, Hua S, Ligorio M, et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature. 2013;496(7443):101–5.PubMed CentralPubMedView ArticleGoogle Scholar
- Kong B, Qia C, Erkan M, Kleeff J, Michalski CW. Overview on how oncogenic Kras promotes pancreatic carcinogenesis by inducing low intracellular ROS levels. Front Physiol. 2013;4:246.PubMed CentralPubMedView ArticleGoogle Scholar
- Lyssiotis CA, Son J, Cantley LC, Kimmelman AC. Pancreatic cancers rely on a novel glutamine metabolism pathway to maintain redox balance. Cell Cycle. 2013;12(13):1987–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Chakrabarti G, Gerber DE, Boothman DA. Expanding antitumor therapeutic windows by targeting cancer-specific nicotinamide adenine dinucleotide phosphate-biogenesis pathways. Clin Pharmacol: Advances and Applications. 2015;7:57–68.Google Scholar
- Fisher GH, Wellen SL, Klimstra D, Lenczowski JM, Tichelaar JW, Lizak MJ, et al. Induction and apoptotic regression of lung adenocarcinomas by regulation of a K-Ras transgene in the presence and absence of tumor suppressor genes. Genes Dev. 2001;15(24):3249–62.PubMed CentralPubMedView ArticleGoogle Scholar
- Luo B, Cheung HW, Subramanian A, Sharifnia T, Okamoto M, Yang X, et al. Highly parallel identification of essential genes in cancer cells. Proc Natl Acad Sci U S A. 2008;105(51):20380–5.PubMed CentralPubMedView ArticleGoogle Scholar
- Mitsuishi Y, Taguchi K, Kawatani Y, Shibata T, Nukiwa T, Aburatani H, et al. Nrf2 redirects glucose and glutamine into anabolic pathways in metabolic reprogramming. Cancer Cell. 2012;22(1):66–79.PubMedView ArticleGoogle Scholar
- DeNicola GM, Karreth FA, Humpton TJ, Gopinathan A, Wei C, Frese K, et al. Oncogene-induced Nrf2 transcription promotes ROS detoxification and tumorigenesis. Nature. 2011;475(7354):106–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Gross MI, Demo SD, Dennison JB, Chen L, Chernov-Rogan T, Goyal B, et al. Antitumor activity of the glutaminase inhibitor CB-839 in triple-negative breast cancer. Mol Cancer Ther. 2014;13(4):890–901.PubMedView ArticleGoogle Scholar
- Cancer Genome Atlas Research N. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511(7511):543–50.View ArticleGoogle Scholar
- Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–7.PubMed CentralPubMedView ArticleGoogle Scholar
- Gyorffy B, Surowiak P, Budczies J, Lanczky A. Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer. PLoS One. 2013;8(12):e82241.PubMed CentralPubMedView ArticleGoogle Scholar
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