SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION doi: /nature22359 Supplementary Discussion KRAS and regulate central carbon metabolism, including pathways supplied by abundant fuels like glucose and glutamine. Under control of signaling pathways regulated by KRAS and, fuels are acquired and allocated into pathways that produce energy, maintain redox homeostasis, and allow simple fuels to be converted into macromolecules like fatty acids and lipids; these are the three major functions of metabolic reprogramming in cancer cells 1. The many roles of KRAS and in these metabolic processes have been studied extensively Our targeted metabolomic analysis was designed to provide an overview of metabolites from these crucial central metabolic pathways while also reporting on other, less well-understood pathways. As expected, we did observe alterations of many metabolites from central carbon pathways like glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle and fatty acid oxidation between K and KL cells. For example, although it is not possible to infer metabolic flux from steady state metabolite levels, it is interesting that every detected metabolite from or closely related to the TCA cycle was altered in KL cell lines, including depletion of α-ketoglutarate, 2-hydroxyglutarate, succinate, fumarate and malate (see metabolites with VIP scores over 1.0 in Fig. 1a and Supplementary Data Table 2). This metabolomics platform also reports on many pathways beyond central carbon and fuel metabolism, particularly pathways involving nitrogenous metabolites. Little is known about the role of in regulating these other pathways. For the purposes of this study, we consider nitrogen-related pathways to include synthesis and degradation of nucleotides; synthesis and degradation of amino acids; glutathione biosynthesis and metabolism; the urea cycle; and nitrogen-containing intermediates in one-carbon metabolism such as betaine and choline. We find metabolites from these pathways to be prominently altered between K and KL cell lines and tumors, with a number of metabolites altered both in cultured cells and in tumors from human subjects (Fig. 1a and Extended Data Fig. 3). References 1 DeBerardinis, R. J. & Chandel, N. S. Fundamentals of cancer metabolism. Sci Adv 2, e (2016). 2 Son, J. et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature 496, , doi: /nature12040 (2013). 3 Ying, H. et al. Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell 149, (2012). 4 Jeon, S.-M., Chandel, N. S. & Hay, N. AMPK regulates NADPH homeostasis to promote tumour 1

2 RESEARCH SUPPLEMENTARY INFORMATION glucose metabolism. Cell 149, (2012). 4 Jeon, S.-M., Chandel, N. S. & Hay, N. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature 485, (2012). 5 Kamphorst, J. J. et al. Human pancreatic cancer tumors are nutrient poor and tumor cells actively scavenge extracellular protein. Cancer Res 75, , doi: / can (2015). 6 Guo, J. Y. et al. Autophagy provides metabolic substrates to maintain energy charge and nucleotide pools in Ras-driven lung cancer cells. Genes Dev 30, (2016). 7 Yun, J. et al. Glucose deprivation contributes to the development of KRAS pathway mutations in tumor cells. Science (New York, N Y ) 325, (2009). 8 Commisso, C. et al. Macropinocytosis of protein is an amino acid supply route in Rastransformed cells. Nature 497, (2013). 9 Faubert, B. et al. Loss of the tumor suppressor promotes metabolic reprogramming of cancer cells via HIF-1alpha. Proceedings of the National Academy of Sciences of the United States of America 111, (2014). 10 Shackelford, D. B. et al. inactivation dictates therapeutic response of non-small cell lung cancer to the metabolism drug phenformin. Cancer Cell 23, (2013). 2

3 SUPPLEMENTARY INFORMATION RESEARCH Figure 2e Figure 3b ASS1 43 Figure 2f 17 Acc A549 H460 ASS1 Figure 4c Figure 4d γh2ax (S139) γh2ax (S139) W W W. N A T U R E. C O M / N A T U R E 3

4 RESEARCH SUPPLEMENTARY INFORMATION Extended Data Figure 4a marker Extended Data Figure 5d-bottom marker Acc NOS3 Extended Data Figure 5g Extended Data Figure 4h marker H CA AMPK Extended Data Figure 5i Extended Data Figure 5b Extended Data Figure 5h H460 ps6 AMPK Extended Data Figure 5c p4e-bp1 Extended Data Figure 5j ps6 55 A549 ps6 Extended Data Figure 5d-top p4e-bp W W W. N A T U R E. C O M / N A T U R E

5 SUPPLEMENTARY INFORMATION RESEARCH Extended Data Figure 6e Extended Data Figure 7a H1755 CREB1 FOXA1 H1355 H460 H2073 Extended Data Figure 7g H1395 H1437 Extended Data Figure 7e Hcc515 H1993 Extended Data Figure 7h H2023 TEAD4 H1755 H2073 H2023 Extended Data Figure 8a Extended Data Figure 8b W W W. N A T U R E. C O M / N A T U R E 5

6 RESEARCH SUPPLEMENTARY INFORMATION Extended Data Figure 9c Extended Data Figure 9d, Left CAD CAD pcad pampk p4ebp1 Extended Data Figure 9d,Right CAD Extended Data Figure 9h γh2ax (S139) Extended Data Figure 10d CAD CREB1 FOXA1 Extended Data Figure 10e γh2ax (S139) TEAD4 6

7 SUPPLEMENTARY INFORMATION RESEARCH Supplementary Information Table 1: Targeted metabolomics in K and KL cells. A set of 111 metabolites was monitored in five cell lines each with the K or KL genotype (see row labeled Class ). Average normalized metabolite abundances are shown for each cell line and for the average of all K and KL cell lines. Average fold changes and statistical assessment of metabolomics differences between the two genotypes are in columns BE and BF. Supplementary Information Table 2: Variable Importance in the Projection (VIP) analysis of metabolomic differences between K and KL cells. Primary data used in the analysis are in Supplementary Information Table 1. Nitrogen-related metabolites are highlighted in orange here and in red on Fig. 1a. Metabolites with p values <0.01 after Bonferroni correction are in green. Supplementary Information Table 3: Targeted metabolomics in K and KL human NSCLC tissues. A set of 133 metabolites was monitored in seven K and four KL tumors (see column labeled Class ). Multiple fragments of each tumor were analyzed by metabolomics. Average normalized metabolite abundances are shown for all K and KL fragments in columns CP and CQ. Statistical assessment and average fold changes of abundance differences between the genotypes are in columns CR and CS. Supplementary Information Table 4: VIP analysis of metabolomic differences between K and KL human NSCLC. Primary data used in the analysis are in Supplementary Information Table 3. Nitrogen-related metabolites are highlighted in orange here and in red on Extended Data Fig. 3b. Metabolites with p values <0.01 after Bonferroni correction are in green. Supplementary Information Table 5: KRAS and STK11 mutation status of 203 cell lines. This Table lists the names and KRAS/STK11 mutation status for the 203 cell lines used in Fig. 1c and Extended Data Fig. 4b. Supplementary Information Table 6: Expression of genes related to the urea cycle and metabolism of amino groups in K and KL cells. Microarray analysis of gene expression was performed in 203 cell lines. Relative mrna abundance is displayed for 29 genes from the Enzymes in Urea Cycle and Metabolism of Amino Group gene set, plus NOS1 and NOS2. Expression distributions for eight genes in this pathway are shown for each genotype in Fig. 1c and Extended Data Fig. 1b. 7

8 RESEARCH SUPPLEMENTARY INFORMATION Supplementary Information Table 7: Classification and mutation status of 94 lung cancer cell lines. This Table lists the names, biological characteristics and KRAS/ classification for the 94 cell lines used in Fig. 2a and Extended Data Fig. 5a. Supplementary Information Table 8: Correlations between mrna abundance and 176 proteins/phosphoproteins. All cell lines listed in Supplementary Information Table 7 were analyzed by Illumina BeadArray for genome-wide mrna abundance and by a reverse-phase protein array (RPPA) containing antibodies against 176 proteins and phosphoproteins, as described in the main text. This Table displays the correlation coefficients (r) and p values for correlations between mrna and the abundance of each of the RPPA targets. is by far the most strongly anticorrelated protein with mrna. Supplementary Information Table 9: Correlations between protein abundance and genome-wide mrna abundance. All cell lines in Supplementary Information Table 7 were analyzed by Illumina BeadArray for genome-wide mrna abundance and by a reverse-phase protein array (RPPA) containing antibodies against 176 proteins and phosphoproteins, as described in the main text. This Table displays the correlation coefficients (r) and p values for correlations between protein abundance as assessed by RPPA and all 19,579 transcripts detected by BeadArray. STK11, the gene encoding, was the 5 th most strongly correlated transcript, and was the second most anti-correlated transcript. Supplementary Information Table 10: This table lists precursor and product ions (Q1 and Q3, respectively), retention time, dwell time, declustering potential (DP), collision energy (CE) and Collision Cell Exit Potential (CXP) for each transition of thymidine. 8