Skip to main content
SpringerLink
Log in

PCDH11X mutation as a potential biomarker for immune checkpoint therapies in lung adenocarcinoma

  • Original Article
  • Published:
Journal of Molecular Medicine Aims and scope Submit manuscript

Abstract

Immune checkpoint inhibitors (ICIs) have achieved impressive success in lung adenocarcinoma (LUAD). However, the response to ICIs varies among patients, and predictive biomarkers are urgently needed. PCDH11X is frequently mutated in LUAD, while its role in ICI treatment is unclear. In this study, we curated genomic and clinical data of 151 LUAD patients receiving ICIs from three independent cohorts. Relations between PCDH11X and treatment outcomes of ICIs were examined. A melanoma cohort collected from five published studies, a pan-cancer cohort, and non-ICI-treated TCGA-LUAD cohort were also examined to investigate whether PCDH11X mutation is a specific predictive biomarker for LUAD ICI treatment. Among the three ICI-treated LUAD cohorts, PCDH11X mutation (PCDH11X-MUT) was associated with better clinical response compared to wild-type PCDH11X (PCDH11X-WT). While in ICI-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and the non-ICI-treated TCGA-LUAD cohort, no significant differences in overall survival (OS) were observed between the PCDH11X-MUT and PCDH11X-WT groups. PCDH11X mutation was associated with increased PD-L1 expression, tumor mutation burden (TMB), neoantigen load, DNA damage repair (DDR) mutations, and hot tumor microenvironment in TCGA-LUAD cohort. Our findings suggested that the PCDH11X mutation might serve as a specific biomarker to predict the efficacy of ICIs for LUAD patients. Considering the relatively small sample size of ICI-treated cohorts, future research with larger cohorts and prospective clinical trials will be essential for validating and further exploring the role of PCDH11X mutation in the context of immunotherapy outcomes in LUAD.

Key messages

  • PCDH11X mutation is associated with better clinical response compared to wild type PCDH11X in three ICIs-treated LUAD cohorts.

  • In ICIs-treated melanoma cohort, the pan-cancer cohort excluding LUAD, and non-ICIs-treated TCGA-LUAD cohorts PCDH11X mutation is not associated with better clinical response, suggesting PCDH11X mutation might be a specific biomarker to predict the efficacy of ICIs treatment for LUAD patients.

  • PCDH11X mutation is associated with increased PD-L1 expression, tumor mutation burden, and neoantigen load in TCGA-LUAD cohort.

  • PCDH11X mutation is associated with hot tumor microenvironment in TCGA-LUAD cohort.

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

Sources for all data used in this study were described in the “Materials and Methods” section. These datasets were accessible freely and can be found in public databases or supplementary material of published articles.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660

    Article  CAS  PubMed  Google Scholar 

  2. Schabath MB, Cote ML (2019) Cancer progress and priorities: lung cancer. Cancer Epidemiol Biomark Prev 28:1563–1579. https://doi.org/10.1158/1055-9965.EPI-19-0221

    Article  Google Scholar 

  3. Leiter A, Veluswamy RR, Wisnivesky JP (2023) The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol 20:624–639. https://doi.org/10.1038/s41571-023-00798-3

    Article  PubMed  Google Scholar 

  4. Han X, Li F, Fang Z, Gao Y, Li F, Fang R, Yao S, Sun Y, Li L, Zhang W et al (2014) Transdifferentiation of lung adenocarcinoma in mice with Lkb1 deficiency to squamous cell carcinoma. Nat commun. https://doi.org/10.1038/ncomms4261

    Article  PubMed  PubMed Central  Google Scholar 

  5. Quintanal-Villalonga A, Taniguchi H, Zhan YA, Hasan MM, Chavan SS, Meng F, Uddin F, Allaj V, Manoj P, Shah NS et al (2021) Comprehensive molecular characterization of lung tumors implicates AKT and MYC signaling in adenocarcinoma to squamous cell transdifferentiation. J hematol oncol. https://doi.org/10.1186/s13045-021-01186-z

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhu L, Liu Y, Gao H, Liu J, Zhou Q, Luo F (2022) Case report: partial response following nivolumab plus docetaxel in a patient with EGFR exon 20 deletion/insertion (p.N771delinsGF) mutant lung adenocarcinoma transdifferentiated from squamous cell carcinoma. Front cell dev biol. https://doi.org/10.3389/fcell.2021.755135

    Article  PubMed  PubMed Central  Google Scholar 

  7. Howlader N, Forjaz G, Mooradian MJ, Meza R, Kong CY, Cronin KA, Mariotto AB, Lowy DR, Feuer EJ (2020) The effect of advances in lung-cancer treatment on population mortality. N Engl J Med 383:640–649. https://doi.org/10.1056/NEJMoa1916623

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Paz-Ares L, Luft A, Vicente D, Tafreshi A, Gümüş M, Mazières J, Hermes B, Çay Şenler F, Csőszi T, Fülöp A et al (2018) Pembrolizumab plus chemotherapy for squamous non–small-cell lung cancer. N Engl J Med 379:2040–2051. https://doi.org/10.1056/NEJMoa1810865

    Article  CAS  PubMed  Google Scholar 

  9. Lu S, Wang J, Cheng Y, Mok T, Chang J, Zhang L, Feng J, Tu H-Y, Wu L, Zhang Y et al (2021) Nivolumab versus docetaxel in a predominantly Chinese patient population with previously treated advanced non-small cell lung cancer: 2-year follow-up from a randomized, open-label, phase 3 study (CheckMate 078). Lung Cancer 152:7–14. https://doi.org/10.1016/j.lungcan.2020.11.013

    Article  CAS  PubMed  Google Scholar 

  10. Jenkins RW, Barbie DA, Flaherty KT (2018) Mechanisms of resistance to immune checkpoint inhibitors. Br J Cancer 118:9–16. https://doi.org/10.1038/bjc.2017.434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zeng H, Tong F, Bin Y, Peng L, Gao X, Xia X, Yi X, Dong X (2022) The predictive value of PAK7 mutation for immune checkpoint inhibitors therapy in non-small cell cancer. Front Immunol 13:834142. https://doi.org/10.3389/fimmu.2022.834142

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Van Roy FJNRC (2014) Beyond E-cadherin: roles of other cadherin superfamily members in cancer. 14: 121–134

  13. Rouget-Quermalet V, Giustiniani J, Marie-Cardine A, Beaud G, Besnard F, Loyaux D, Ferrara P, Leroy K, Shimizu N, Gaulard P et al (2006) Protocadherin 15 (PCDH15): a new secreted isoform and a potential marker for NK/T cell lymphomas. Oncogene 25:2807–2811. https://doi.org/10.1038/sj.onc.1209301

    Article  CAS  PubMed  Google Scholar 

  14. Vazquez-Cintron EJ, Monu NR, Burns JC, Blum R, Chen G, Lopez P, Ma J, Radoja S, Frey AB (2012) Protocadherin-18 is a novel differentiation marker and an inhibitory signaling receptor for CD8+ effector memory T cells. PLoS ONE 7:e36101. https://doi.org/10.1371/journal.pone.0036101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Frey AB (2017) The inhibitory signaling receptor protocadherin-18 regulates tumor-infiltrating CD8(+) T-cell function. Cancer Immunol Res 5:920–928. https://doi.org/10.1158/2326-6066.CIR-17-0187

    Article  CAS  PubMed  Google Scholar 

  16. Zhu G, Ren D, Lei X, Shi R, Zhu S, Zhou N, Zu L, Mello RA, Chen J, Xu S (2021) Mutations associated with no durable clinical benefit to immune checkpoint blockade in non-S-cell lung cancer. Cancers (Basel). https://doi.org/10.3390/cancers13061397

    Article  PubMed  PubMed Central  Google Scholar 

  17. Feng Z, Yin Y, Liu B, Zheng Y, Shi D, Zhang H, Qin J (2022) Prognostic and immunological role of FAT family genes in non-small cell lung cancer. Cancer Control 29:10732748221076682. https://doi.org/10.1177/10732748221076682

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hellmann MD, Nathanson T, Rizvi H, Creelan BC, Sanchez-Vega F, Ahuja A, Ni A, Novik JB, Mangarin LMB, Abu-Akeel M et al (2018) Genomic features of response to combination immunotherapy in patients with advanced non-small-cell lung cancer. Cancer Cell 33(843–852):e844. https://doi.org/10.1016/j.ccell.2018.03.018

    Article  CAS  Google Scholar 

  19. Miao D, Margolis CA, Vokes NI, Liu D, Taylor-Weiner A, Wankowicz SM, Adeegbe D, Keliher D, Schilling B, Tracy A et al (2018) Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat Genet 50:1271–1281. https://doi.org/10.1038/s41588-018-0200-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Lee W, Yuan J, Wong P, Ho TS et al (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348:124–128. https://doi.org/10.1126/science.aaa1348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Liu D, Schilling B, Liu D, Sucker A, Livingstone E, Jerby-Arnon L, Zimmer L, Gutzmer R, Satzger I, Loquai C et al (2019) Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med 25:1916–1927. https://doi.org/10.1038/s41591-019-0654-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G et al (2016) Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165:35–44. https://doi.org/10.1016/j.cell.2016.02.065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, Hodi FS, Martin-Algarra S, Mandal R, Sharfman WH et al (2017) Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171(934–949):e916. https://doi.org/10.1016/j.cell.2017.09.028

    Article  CAS  Google Scholar 

  24. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, Sucker A, Hillen U, Foppen MHG, Goldinger SM et al (2015) Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350:207–211. https://doi.org/10.1126/science.aad0095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh LA, Postow MA, Wong P, Ho TS et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371:2189–2199. https://doi.org/10.1056/NEJMoa1406498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP (2018) Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 28:1747–1756. https://doi.org/10.1101/gr.239244.118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gu Z, Eils R, Schlesner M (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32:2847–2849. https://doi.org/10.1093/bioinformatics/btw313

    Article  CAS  PubMed  Google Scholar 

  28. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I et al (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44:e71. https://doi.org/10.1093/nar/gkv1507

    Article  CAS  PubMed  Google Scholar 

  29. Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G (2011) GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. https://doi.org/10.1186/gb-2011-12-4-r41

    Article  PubMed  PubMed Central  Google Scholar 

  30. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA et al (2018) The immune landscape of cancer. Immunity 48(812–830):e814. https://doi.org/10.1016/j.immuni.2018.03.023

    Article  CAS  Google Scholar 

  31. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Yang T-HO, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA et al (2018) The immune landscape of cancer. Immunity. https://doi.org/10.1016/j.immuni.2018.03.023

    Article  PubMed  PubMed Central  Google Scholar 

  32. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616

    Article  CAS  PubMed  Google Scholar 

  33. Ravi A, Hellmann MD, Arniella MB, Holton M, Freeman SS, Naranbhai V, Stewart C, Leshchiner I, Kim J, Akiyama Y et al (2023) Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. Nat Genet 55:807–819. https://doi.org/10.1038/s41588-023-01355-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Choucair K, Morand S, Stanbery L, Edelman G, Dworkin L, Nemunaitis J (2020) TMB: a promising immune-response biomarker, and potential spearhead in advancing targeted therapy trials. Cancer Gene Ther 27:841–853. https://doi.org/10.1038/s41417-020-0174-y

    Article  CAS  PubMed  Google Scholar 

  35. Bai R, Lv Z, Xu D, Cui J (2020) Predictive biomarkers for cancer immunotherapy with immune checkpoint inhibitors. Biomarker research 8:34. https://doi.org/10.1186/s40364-020-00209-0

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jiang M, Jia K, Wang L, Li W, Chen B, Liu Y, Wang H, Zhao S, He Y, Zhou C (2021) Alterations of DNA damage response pathway: biomarker and therapeutic strategy for cancer immunotherapy. Acta pharmaceutica Sinica B 11:2983–2994. https://doi.org/10.1016/j.apsb.2021.01.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Long J, Wang D, Wang A, Chen P, Lin Y, Bian J, Yang X, Zheng M, Zhang H, Zheng Y et al (2022) A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape. Genome medicine 14:20. https://doi.org/10.1186/s13073-022-01024-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Duan Q, Zhang H, Zheng J, Zhang L (2020) Turning cold into hot: firing up the tumor microenvironment. Trends in cancer 6:605–618. https://doi.org/10.1016/j.trecan.2020.02.022

    Article  CAS  PubMed  Google Scholar 

  39. Nie M, Yao K, Zhu X, Chen N, Xiao N, Wang Y, Peng B, Yao L, Li P, Zhang P et al (2021) Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma. Nat Commun 12:6479. https://doi.org/10.1038/s41467-021-26685-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Zhang X, Young HA (2002) PPAR and immune system–what do we know? Int Immunopharmacol 2:1029–1044. https://doi.org/10.1016/s1567-5769(02)00057-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wu Y, Liu Z, Tang D, Liu H, Luo S, Stinchcombe TE, Glass C, Su L, Lin L, Christiani DC et al (2021) Potentially functional variants of HBEGF and ITPR3 in GnRH signaling pathway genes predict survival of non-small cell lung cancer patients. Translational research : the journal of laboratory and clinical medicine 233:92–103. https://doi.org/10.1016/j.trsl.2020.12.009

    Article  CAS  PubMed  Google Scholar 

  42. Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, Sher X, Liu XQ, Lu H, Nebozhyn M et al (2018) Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science. https://doi.org/10.1126/science.aar3593

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hellmann MD, Ciuleanu TE, Pluzanski A, Lee JS, Otterson GA, Audigier-Valette C, Minenza E, Linardou H, Burgers S, Salman P et al (2018) Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N Engl J Med 378:2093–2104. https://doi.org/10.1056/NEJMoa1801946

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tunger A, Sommer U, Wehner R, Kubasch AS, Grimm MO, Bachmann MP, Platzbecker U, Bornhauser M, Baretton G, Schmitz M (2019) The evolving landscape of biomarkers for anti-PD-1 or anti-PD-L1 therapy. J clin med. https://doi.org/10.3390/jcm8101534

    Article  PubMed  PubMed Central  Google Scholar 

  45. Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, Fan H, Shen H, Way GP, Greene CS et al (2018) Genomic and molecular landscape of DNA damage repair deficiency across The Cancer Genome Atlas. Cell Rep 23(239–254):e236. https://doi.org/10.1016/j.celrep.2018.03.076

    Article  CAS  Google Scholar 

  46. Tubbs A, Nussenzweig A (2017) Endogenous DNA damage as a source of genomic instability in cancer. Cell 168:644–656. https://doi.org/10.1016/j.cell.2017.01.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Khosravi GR, Mostafavi S, Bastan S, Ebrahimi N, Gharibvand RS, Eskandari N (2024) Immunologic tumor microenvironment modulators for turning cold tumors hot. Cancer commun. https://doi.org/10.1002/cac2.12539

    Article  Google Scholar 

  48. Dunn GP, Bruce AT, Sheehan KC, Shankaran V, Uppaluri R, Bui JD, Diamond MS, Koebel CM, Arthur C, White JM et al (2005) A critical function for type I interferons in cancer immunoediting. Nat Immunol 6:722–729. https://doi.org/10.1038/ni1213

    Article  CAS  PubMed  Google Scholar 

  49. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, Albright A, Cheng JD, Kang SP, Shankaran V et al (2017) IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127:2930–2940. https://doi.org/10.1172/JCI91190

    Article  PubMed  PubMed Central  Google Scholar 

  50. Baumjohann D, Brossart P (2021) T follicular helper cells: linking cancer immunotherapy and immune-related adverse events. J immunother cancer. https://doi.org/10.1136/jitc-2021-002588

    Article  PubMed  PubMed Central  Google Scholar 

  51. Sun Y, Liu L, Fu Y, Liu Y, Gao X, Xia X, Zhu D, Wang X, Zhou X (2023) Metabolic reprogramming involves in transition of activated/resting CD4+ memory T cells and prognosis of gastric cancer. Front immunol. https://doi.org/10.3389/fimmu.2023.1275461

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mayoux M, Roller A, Pulko V, Sammicheli S, Chen S, Sum E, Jost C, Fransen MF, Buser RB, Kowanetz M et al (2020) Dendritic cells dictate responses to PD-L1 blockade cancer immunotherapy. Sci Transl Med. https://www.science.org/doi/10.1126/scitranslmed.aav7431

  53. Peng Q, Qiu X, Zhang Z, Zhang S, Zhang Y, Liang Y, Guo J, Peng H, Chen M, Fu Y-X et al (2020) PD-L1 on dendritic cells attenuates T cell activation and regulates response to immune checkpoint blockade. Nat commun. https://doi.org/10.1038/s41467-020-18570-x

    Article  PubMed  PubMed Central  Google Scholar 

  54. Somasundaram R, Connelly T, Choi R, Choi H, Samarkina A, Li L, Gregorio E, Chen Y, Thakur R, Abdel-Mohsen M et al (2021) Tumor-infiltrating mast cells are associated with resistance to anti-PD-1 therapy. Nat commun. https://doi.org/10.1038/s41467-020-20600-7

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wang C, Zheng X, Zhang J, Jiang X, Wang J, Li Y, Li X, Shen G, Peng J, Zheng P et al (2023) CD300ld on neutrophils is required for tumour-driven immune suppression. Nature 621:830–839. https://doi.org/10.1038/s41586-023-06511-9

    Article  CAS  PubMed  Google Scholar 

  56. Wherry EJ, Kurachi M (2015) Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol 15:486–499. https://doi.org/10.1038/nri3862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. van Roy F (2014) Beyond E-cadherin: roles of other cadherin superfamily members in cancer. Nat Rev Cancer 14:121–134. https://doi.org/10.1038/nrc3647

    Article  CAS  PubMed  Google Scholar 

  58. Bader JE, Voss K, Rathmell JC (2020) Targeting metabolism to improve the tumor microenvironment for cancer immunotherapy. Mol Cell 78:1019–1033. https://doi.org/10.1016/j.molcel.2020.05.034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by the Collaborative Innovation Major Project of Zhengzhou (Grant No. 20XTZX08017), National Key Research and Development Program of China (Grant No. 2018YFE0102100), the UK-China Collaboration Fund to tackle AMR (Grant No. TS/S00887X/1), and National Science and Technology Major Project (Grant No. 2018ZX10305409-005).

Author information

Author notes

  1. Manjiao Liu, Meijia Yang and Bei Zhang have contributed equally to this work.

Authors and Affiliations

  1. National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China

    Meijia Yang & Jie Zhao

  2. State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing, 210042, China

    Manjiao Liu, Bei Zhang, Sijian Xia, Jie Zhao, Linlin Yan, Yong Ren & Hao Guo

  3. Nanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing, 210042, China

    Manjiao Liu, Bei Zhang, Sijian Xia, Jie Zhao, Linlin Yan, Yong Ren & Hao Guo

Authors
  1. Manjiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meijia Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sijian Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Linlin Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yong Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hao Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Hao Guo, Manjiao Liu, and Jie Zhao (from The First Affiliated Hospital of Zhengzhou University) contributed to the study conception and design. Data collection and analysis were performed by Manjiao Liu, Meijia Yang, Bei Zhang, Sijian Xia, Jie Zhao (from Jiangsu Simcere Diagnostics Co., Ltd.), Linlin Yan, and Yong Ren. The manuscript was written by Bei Zhang, Manjiao Liu, and Sijian Xia. The project was supervised by Jie Zhao (from The First Affiliated Hospital of Zhengzhou University) and Hao Guo. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Hao Guo or Jie Zhao.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

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

Liu, M., Yang, M., Zhang, B. et al. PCDH11X mutation as a potential biomarker for immune checkpoint therapies in lung adenocarcinoma. J Mol Med (2024). https://doi.org/10.1007/s00109-024-02450-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00109-024-02450-8

Keywords

Search

Navigation