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Original Article | Open Access

A novel recurrence-associated metabolic prognostic model for risk stratification and therapeutic response prediction in patients with stage Ⅰ lung adenocarcinoma

Chengming Liu1Sihui Wang1Sufei Zheng1Xinfeng Wang1Jianbin Huang1Yuanyuan Lei1Shuangshuang Mao1Xiaoli Feng2Nan Sun1 ( )Jie He1 ( )
Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Abstract

Objective

The proportion of patients with stage Ⅰ lung adenocarcinoma (LUAD) has dramatically increased with the prevalence of low-dose computed tomography use for screening. Up to 30% of patients with stage Ⅰ LUAD experience recurrence within 5 years after curative surgery. A robust risk stratification tool is urgently needed to identify patients who might benefit from adjuvant treatment.

Methods

In this first investigation of the relationship between metabolic reprogramming and recurrence in stage Ⅰ LUAD, we developed a recurrence-associated metabolic signature (RAMS). This RAMS was based on metabolism-associated genes to predict cancer relapse and overall prognoses of patients with stage Ⅰ LUAD. The clinical significance and immune landscapes of the signature were comprehensively analyzed.

Results

Based on a gene expression profile from the GSE31210 database, functional enrichment analysis revealed a significant difference in metabolic reprogramming that distinguished patients with stage Ⅰ LUAD with relapse from those without relapse. We then identified a metabolic signature (i.e., RAMS) represented by 2 genes (ACADM and RPS8) significantly related to recurrence-free survival and overall survival times of patients with stage Ⅰ LUAD using transcriptome data analysis of a training set. The training set was well validated in a test set. The discriminatory power of the 2 gene metabolic signature was further validated using protein values in an additional independent cohort. The results indicated a clear association between a high risk score and a very poor patient prognosis. Stratification analysis and multivariate Cox regression analysis showed that the RAMS was an independent prognostic factor. We also found that the risk score was positively correlated with inflammatory response, the antigen-presenting process, and the expression levels of many immunosuppressive checkpoint molecules (e.g., PD-L1, PD-L2, B7-H3, galectin-9, and FGL-1). These results suggested that high risk patients had immune response suppression. Further analysis revealed that anti-PD-1/PD-L1 immunotherapy did not have significant benefits for high risk patients. However, the patients could respond better to chemotherapy.

Conclusions

This study is the first to highlight the relationship between metabolic reprogramming and recurrence in stage Ⅰ LUAD, and is the first to also develop a clinically feasible signature. This signature may be a powerful prognostic tool and help further optimize the cancer therapy paradigm.

Electronic Supplementary Material

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References

1

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019; 69: 7-34.

2

Chen Z, Fillmore CM, Hammerman PS, Kim CF, Wong KK. Non-small-cell lung cancers: a heterogeneous set of diseases. Nat Rev Cancer. 2014; 14: 535-46.

3

Tanoue LT, Tanner NT, Gould MK, Silvestri GA. Lung cancer screening. Am J Respir Crit Care Med. 2015; 191: 19-33.

4

Wood DE, Kazerooni EA, Baum SL, Eapen GA, Ettinger DS, Hou L, et al. Lung cancer screening, version 3.2018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2018; 16: 412-41.

5

Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol. 2016; 11: 39-51.

6

Tsao AS, Scagliotti GV, Bunn Jr PA, Carbone DP, Warren GW, Bai C, et al. Scientific advances in lung cancer 2015. J Thorac Oncol. 2016; 11: 613-38.

7

Ettinger DS, Aisner DL, Wood DE, Akerley W, Bauman J, Chang JY, et al. NCCN guidelines insights: non-small cell lung cancer, version 5.2018. J Natl Compr Canc Netw. 2018; 16: 807-21.

8

Fujimoto T, Cassivi SD, Yang P, Barnes SA, Nichols FC, Deschamps C, et al. Completely resected N1 non-small cell lung cancer: factors affecting recurrence and long-term survival. J Thorac Cardiovasc Surg. 2006; 132: 499-506.

9

Pignon JP, Tribodet H, Scagliotti GV, Douillard JY, Shepherd FA, Stephens RJ, et al. Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group. J Clin Oncol. 2008; 26: 3552-9.

10

Sangha R, Price J, Butts CA. Adjuvant therapy in non-small cell lung cancer: current and future directions. Oncologist. 2010; 15: 862-72.

11

Yang Y, Mao Y, Yang L, He J, Gao S, Mu J, et al. Prognostic factors in curatively resected pathological stage Ⅰ lung adenocarcinoma. J Thorac Dis. 2017; 9: 5267-77.

12

Saynak M, Veeramachaneni NK, Hubbs JL, Nam J, Qaqish BF, Bailey JE, et al. Local failure after complete resection of N0-1 non-small cell lung cancer. Lung Cancer. 2011; 71: 156-65.

13

Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011; 144: 646-74.

14

Peng X, Chen Z, Farshidfar F, Xu X, Lorenzi PL, Wang Y, et al. Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers. Cell Rep. 2018; 23: 255-69.e4.

15

DeBerardinis RJ, Chandel NS. Fundamentals of cancer metabolism. Sci Adv. 2016; 2: e1600200.

16

Vander Heiden MG, DeBerardinis RJ. Understanding the intersections between metabolism and cancer biology. Cell. 2017; 168: 657-69.

17

Chen PH, Cai L, Huffman K, Yang C, Kim J, Faubert B, et al. Metabolic diversity in human non-small cell lung cancer cells. Mol Cell. 2019; 76: 838-51.e5.

18

Woo T, Okudela K, Yazawa T, Wada N, Ogawa N, Ishiwa N, et al. Prognostic value of KRAS mutations and Ki-67 expression in stage Ⅰ lung adenocarcinomas. Lung Cancer. 2009; 65: 355-62.

19

Kim P, Cheng F, Zhao J, Zhao Z. ccmGDB: a database for cancer cell metabolism genes. Nucleic Acids Res. 2016; 44: D959-68.

20

Long J, Wang A, Bai Y, Lin J, Yang X, Wang D, et al. Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine. 2019; 42: 363-74.

21

Liu C, Zheng S, Jin R, Wang X, Wang F, Zang R, et al. The superior efficacy of anti-PD-1/PD-L1 immunotherapy in KRAS-mutant non-small cell lung cancer that correlates with an inflammatory phenotype and increased immunogenicity. Cancer Lett. 2019; 470: 95-105.

22

Alsaleem MA, Ball G, Toss MS, Raafat S, Aleskandarany M, Joseph C, et al. A novel prognostic two-gene signature for triple negative breast cancer. Mod Pathol. 2020; 33: 2208-20.

23

Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12: 453-7.

24

Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015; 21: 938-45.

25

Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018; 24: 1550-8.

26

Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One. 2014; 9: e107468.

27

Geeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 2014; 15: R47.

28

Rody A, Holtrich U, Pusztai L, Liedtke C, Gaetje R, Ruckhaeberle E, et al. T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res. 2009; 11: R15.

29

Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013; 14: 7.

30

Schildberg FA, Klein SR, Freeman GJ, Sharpe AH. Coinhibitory pathways in the B7-CD28 ligand-receptor family. Immunity. 2016; 44: 955-72.

31

Wang J, Sanmamed MF, Datar I, Su TT, Ji L, Sun J, et al. Fibrinogen-like protein 1 is a major immune inhibitory ligand of LAG-3. Cell. 2018; 176: 334-47.e12.

32

Chen L, Flies DB. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat Rev Immunol. 2013; 13: 227-42.

33

Limagne E, Richard C, Thibaudin M, Fumet JD, Truntzer C, Lagrange A, et al. Tim-3/galectin-9 pathway and mMDSC control primary and secondary resistances to PD-1 blockade in lung cancer patients. Oncoimmunology. 2019; 8: e1564505.

34

Black WC, Gareen IF, Soneji SS, Sicks JD, Keeler EB, Aberle DR, et al. Cost-effectiveness of CT screening in the National Lung Screening Trial. N Engl J Med. 2014; 371: 1793-802.

35

Suzuki K, Kadota K, Sima CS, Nitadori J, Rusch VW, Travis WD, et al. Clinical impact of immune microenvironment in stage Ⅰ lung adenocarcinoma: tumor interleukin-12 receptor beta2 (IL-12Rbeta2), IL-7R, and stromal FoxP3/CD3 ratio are independent predictors of recurrence. J Clin Oncol. 2013; 31: 490-8.

36

Hensley CT, Faubert B, Yuan Q, Lev-Cohain N, Jin E, Kim J, et al. Metabolic heterogeneity in human lung tumors. Cell. 2016; 164: 681-94.

37

Faubert B, Li KY, Cai L, Hensley CT, Kim J, Zacharias LG, et al. Lactate metabolism in human lung tumors. Cell. 2017; 171: 358-71.e9.

38

Monaco ME. Fatty acid metabolism in breast cancer subtypes. Oncotarget. 2017; 8: 29487-500.

39

Zhang H, Zou J, Yin Y, Zhang B, Hu Y, Wang J, et al. Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma. PeerJ. 2019; 7: e8096.

40

Niu Z, Shi Q, Zhang W, Shu Y, Yang N, Chen B, et al. Caspase-1 cleaves PPARγ for potentiating the pro-tumor action of TAMs. Nat Commun. 2017; 8: 766.

41

Hao Y, Kong X, Ruan Y, Gan H, Chen H, Zhang C, et al. CDK11p46 and RPS8 associate with each other and suppress translation in a synergistic manner. Biochem Biophys Res Commun. 2011; 407: 169-74.

42

JØnson L, Vikesaa J, Krogh A, Nielsen LK, Hansen T, Borup R, et al. Molecular composition of IMP1 ribonucleoprotein granules. Mol Cell Proteomics. 2007; 6: 798-811.

43

Ruggero D, Pandolfi PP. Does the ribosome translate cancer? Nat Rev Cancer. 2003; 3: 179-92.

44

Holcik M, Sonenberg N. Translational control in stress and apoptosis. Nat Rev Mol Cell Biol. 2005; 6: 318-27.

45

O’Neill LA, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nat Rev Immunol. 2016; 16: 553-65.

Cancer Biology & Medicine
Pages 734-749
Cite this article:
Liu C, Wang S, Zheng S, et al. A novel recurrence-associated metabolic prognostic model for risk stratification and therapeutic response prediction in patients with stage Ⅰ lung adenocarcinoma. Cancer Biology & Medicine, 2021, 18(3): 734-749. https://doi.org/10.20892/j.issn.2095-3941.2020.0397

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Received: 25 July 2020
Accepted: 27 October 2020
Published: 01 August 2021
©2021 Cancer Biology & Medicine.

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