Skip to main content

Advertisement

SpringerLink
Log in

Proteomics-based clustering of lung adenocarcinoma identifies three subtypes with significantly different clinical and molecular features

  • RESEARCH ARTICLE
  • Published:
Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Background

Lung adenocarcinoma (LUAD) is a predominant subtype of lung cancer. Although molecular classification of LUAD has been widely explored, proteomics-based subtyping of LUAD remains scarce.

Methods

We proposed a subtyping method for LUAD based on the expression profiles of 500 proteins with the largest expression variability across LUAD. Furthermore, we comprehensively compared molecular and clinical features among the LUAD subtypes.

Results

Consensus clustering identified three subtypes of LUAD, namely MtE, DrE, and StE. We demonstrated this subtyping method to be reproducible by analyzing two independent LUAD cohorts. MtE was characterized by high enrichment of metabolic pathways, high EGFR mutation rate, low stemness, proliferation, invasion, metastasis and inflammation signatures, favorable prognosis; DrE was characterized by high enrichment of DNA repair pathways, high TP53 mutation rate, and high levels of genomic instability, stemness, proliferation, and intratumor heterogeneity (ITH); and StE was characterized by high enrichment of stroma-related pathways, high KRAS mutation rate, and low levels of genomic instability.

Conclusions

The proteomics-based clustering analysis identified three LUAD subtypes with significantly different molecular and clinical properties. The novel subtyping method offers new perspectives on the cancer biology and holds promise in improving the clinical management of LUAD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Publicly available datasets were analyzed in this study. The CPTAC-LUAD data can be found at: (https://www.linkedomics.org/data_download/CPTAC-LUAD/), and the LUAD-Xu data were from a related publication [13].

References

  1. Huang J, Deng Y, Tin MS, Lok V, Ngai CH, Zhang L, et al. Distribution, risk factors, and temporal trends for lung cancer incidence and mortality: a global analysis. Chest. 2022;161(4):1101–11.https://doi.org/10.1016/j.chest.2021.12.655

    Article  PubMed  Google Scholar 

  2. Xia C, Dong X, Li H, Cao M, Sun D, He S, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J. 2022;135(05):584–90.https://doi.org/10.1097/CM9.0000000000002108

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chen Z, Fillmore CM, Hammerman PS, Kim CF, Wong K-K. Non-small-cell lung cancers: a heterogeneous set of diseases. Nat Rev Cancer. 2014;14(8):535–46.https://doi.org/10.1038/nrc3775

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tavernari D, Battistello E, Dheilly E, Petruzzella AS, Mina M, Sordet-Dessimoz J, et al. Nongenetic evolution drives lung adenocarcinoma spatial heterogeneity and progressionnongenetic evolution of lung adenocarcinoma heterogeneity. Cancer Discov. 2021;11(6):1490–507.https://doi.org/10.1158/2159-8290.CD-20-1274

    Article  CAS  PubMed  Google Scholar 

  5. Li WY, Zhao TT, Xu HM, Wang ZN, Xu YY, Han Y, et al. The role of EGFR mutation as a prognostic factor in survival after diagnosis of brain metastasis in non-small cell lung cancer: a systematic review and meta-analysis. BMC Cancer. 2019;19(1):145.https://doi.org/10.1186/s12885-019-5331-z

    Article  PubMed  PubMed Central  Google Scholar 

  6. Network CGAR. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511(7511):543.https://doi.org/10.1038/nature13385

    Article  CAS  Google Scholar 

  7. Gillette MA, Satpathy S, Cao S, Dhanasekaran SM, Vasaikar SV, Krug K, et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell. 2020;182(1):200–25.https://doi.org/10.1016/j.cell.2020.06.013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Liu Q, Lei J, Zhang X, Wang X. Classification of lung adenocarcinoma based on stemness scores in bulk and single cell transcriptomes. Comput Struct Biotechnol J. 2022;20:1691–701.https://doi.org/10.1016/j.csbj.2022.04.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Menyhárt O, Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J. 2021;19:949–60.https://doi.org/10.1016/j.csbj.2021.01.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Yaffe MB. Why geneticists stole cancer research even though cancer is primarily a signaling disease. Sci Signal. 2019. https://doi.org/10.1126/scisignal.aaw3483.

    Article  PubMed  Google Scholar 

  11. Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics. 2020;17:17.https://doi.org/10.1186/s12014-020-09283-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gry M, Rimini R, Strömberg S, Asplund A, Pontén F, Uhlén M, et al. Correlations between RNA and protein expression profiles in 23 human cell lines. BMC Genomics. 2009;10(1):1–14.https://doi.org/10.1186/1471-2164-10-365

    Article  CAS  Google Scholar 

  13. Xu J-Y, Zhang C, Wang X, Zhai L, Ma Y, Mao Y, et al. Integrative proteomic characterization of human lung adenocarcinoma. Cell. 2020;182(1):245–61.https://doi.org/10.1016/j.cell.2020.05.043

    Article  CAS  PubMed  Google Scholar 

  14. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.https://doi.org/10.1186/1471-2105-14-7

    Article  PubMed  PubMed Central  Google Scholar 

  15. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9(1):1–13.https://doi.org/10.1186/1471-2105-9-559

    Article  CAS  Google Scholar 

  16. Bland JM, Altman DG. Survival probabilities (the Kaplan-Meier method). BMJ. 1998;317(7172):1572.https://doi.org/10.1136/bmj.317.7172.1572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Li M, Zhang Z, Li L, Wang X. An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles. Commun Biol. 2020;3(1):505.https://doi.org/10.1038/s42003-020-01230-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc. 1995;57(1):289–300.https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  Google Scholar 

  19. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47(W1):W191–8.https://doi.org/10.1093/nar/gkz369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sullivan I, Planchard D. Next-Generation EGFR tyrosine kinase inhibitors for treating EGFR-mutant lung cancer beyond first line. Front Med (Lausanne). 2016;3:76.https://doi.org/10.3389/fmed.2016.00076

    Article  PubMed  Google Scholar 

  21. Li W-Y, Zhao T-T, Xu H-M, Wang Z-N, Xu Y-Y, Han Y, et al. The role of EGFR mutation as a prognostic factor in survival after diagnosis of brain metastasis in non-small cell lung cancer: a systematic review and meta-analysis. BMC Cancer. 2019;19(1):1–9.https://doi.org/10.1186/s12885-019-5331-z

    Article  Google Scholar 

  22. Wang X, Sun Q. TP53 mutations, expression and interaction networks in human cancers. Oncotarget. 2017;8(1):624–43.https://doi.org/10.18632/oncotarget.13483

    Article  PubMed  Google Scholar 

  23. Ferguson LR, Chen H, Collins AR, Connell M, Damia G, Dasgupta S, et al. Genomic instability in human cancer: Molecular insights and opportunities for therapeutic attack and prevention through diet and nutrition. Seminars Cancer Biol. 2015. https://doi.org/10.1016/j.semcancer.2015.03.005.

    Article  Google Scholar 

  24. Palmieri G, Colombino M, Cossu A, Marchetti A, Botti G, Ascierto PA. Genetic instability and increased mutational load: which diagnostic tool best direct patients with cancer to immunotherapy? J Transl Med. 2017;15(1):17.https://doi.org/10.1186/s12967-017-1119-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Storchova Z, Pellman D. From polyploidy to aneuploidy, genome instability and cancer. Nat Rev Mol Cell Biol. 2004;5(1):45–54.https://doi.org/10.1038/nrm1276

    Article  CAS  PubMed  Google Scholar 

  26. Postel-Vinay S, Vanhecke E, Olaussen KA, Lord CJ, Ashworth A, Soria J-C. The potential of exploiting DNA-repair defects for optimizing lung cancer treatment. Nat Rev Clin Oncol. 2012;9(3):144–55.https://doi.org/10.1038/nrclinonc.2012.3

    Article  CAS  PubMed  Google Scholar 

  27. Ayob AZ, Ramasamy TS. Cancer stem cells as key drivers of tumour progression. J Biomed Sci. 2018;25(1):20.https://doi.org/10.1186/s12929-018-0426-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501(7467):338–345.https://doi.org/10.1038/nature12625

    Article  CAS  PubMed  Google Scholar 

  29. Clayton NS, Ridley AJ. Targeting Rho GTPase signaling networks in cancer. Front Cell Dev Biol. 2020;8:222.https://doi.org/10.3389/fcell.2020.00222

    Article  PubMed  PubMed Central  Google Scholar 

  30. Inamura K. Lung cancer: understanding its molecular pathology and the 2015 WHO classification. Front Oncol. 2017;7:193.https://doi.org/10.3389/fonc.2017.00193

    Article  PubMed  PubMed Central  Google Scholar 

  31. Biaoxue R, Xiling J, Shuanying Y, Wei Z, Xiguang C, Jinsui W, et al. Upregulation of Hsp90-beta and annexin A1 correlates with poor survival and lymphatic metastasis in lung cancer patients. J Exp Clin Cancer Res. 2012;31(1):70.https://doi.org/10.1186/1756-9966-31-70

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Duan S, Huang W, Liu X, Liu X, Chen N, Xu Q, et al. IMPDH2 promotes colorectal cancer progression through activation of the PI3K/AKT/mTOR and PI3K/AKT/FOXO1 signaling pathways. J Exp Clin Cancer Res. 2018;37(1):304.https://doi.org/10.1186/s13046-018-0980-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. He Y, Zheng Z, Xu Y, Weng H, Gao Y, Qin K, et al. Over-expression of IMPDH2 is associated with tumor progression and poor prognosis in hepatocellular carcinoma. Am J Cancer Res. 2018;8(8):1604–14.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu X, Sato N, Yabushita T, Li J, Jia Y, Tamura M, et al. IMPDH inhibition activates TLR-VCAM1 pathway and suppresses the development of MLL-fusion leukemia. EMBO Mol Med. 2023;15(1):e15631.https://doi.org/10.15252/emmm.202115631

    Article  CAS  PubMed  Google Scholar 

  35. Huang F, Huffman KE, Wang Z, Wang X, Li K, Cai F, et al. Guanosine triphosphate links MYC-dependent metabolic and ribosome programs in small-cell lung cancer. J Clin Invest. 2021. https://doi.org/10.1172/JCI139929.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Huang F, Ni M, Chalishazar MD, Huffman KE, Kim J, Cai L, et al. Inosine monophosphate dehydrogenase dependence in a subset of small cell lung cancers. Cell Metab. 2018;28(3):369–82.https://doi.org/10.1016/j.cmet.2018.06.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the China Pharmaceutical University (grant number 3150120001 to XW).

Author information

Author notes

  1. Rongzhuo Long and Nayila Abulimiti have been contributed equally to this work.

Authors and Affiliations

  1. Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China

    Rongzhuo Long, Nayila Abulimiti & Xiaosheng Wang

  2. Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China

    Rongzhuo Long, Nayila Abulimiti & Xiaosheng Wang

  3. Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China

    Rongzhuo Long, Nayila Abulimiti & Xiaosheng Wang

Authors
  1. Rongzhuo Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nayila Abulimiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaosheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RL: Software, Validation, Formal analysis, Investigation, Data curation, Visualization, Writing—original draft. NA: Software, Validation, Formal analysis, Investigation, Data curation, Visualization. XW: Conceptualization, Methodology, Resources, Investigation, Writing—original draft, Writing—review & editing, Supervision, Project administration, Funding acquisition. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaosheng Wang.

Ethics declarations

Conflict of Interest

The authors declare that they have no competing interests.

Ethical approval

Ethical approval and consent to participate were waived since we used only publicly available data and materials in this study.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 18 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Long, R., Abulimiti, N. & Wang, X. Proteomics-based clustering of lung adenocarcinoma identifies three subtypes with significantly different clinical and molecular features. Clin Transl Oncol 26, 538–548 (2024). https://doi.org/10.1007/s12094-023-03275-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12094-023-03275-6

Keywords

Search

Navigation