Skip to main content

Advertisement

SpringerLink
Log in

Development of nomograms to predict therapeutic response and prognosis of non-small cell lung cancer patients treated with anti-PD-1 antibody

  • Original Article
  • Published:
Cancer Immunology, Immunotherapy Aims and scope Submit manuscript

Abstract

Background

Anti-programmed death-1 (PD-1) antibody changed the treatment of non-small cell lung cancer (NSCLC), however, reliable predictive markers were lacking. We aimed to explore factors associated with response and survival, and develop predictive models.

Methods

This multicenter retrospective study included a training cohort (n = 92) and validation cohort (n = 111) with NSCLC patients received anti-PD-1 antibody monotherapy in eight Chinese hospitals, and a control cohort (n = 124) with NSCLC patients received chemotherapy. Logistic and Cox models were used to identify factors associated with response and survival respectively. Nomograms were developed based on significant factors, and evaluated by Concordance-index (C-index), area under the curve (AUC) and calibration curve.

Result

In training cohort, smoking history (P = 0.027) and higher absolute lymphocyte count (P = 0.038) were associated with response. Female (P < 0.001), age ≥ 65 years (P = 0.004) and higher lactate dehydrogenase (LDH, P < 0.001) were associated with shorter progression-free survival (PFS). Higher LDH (P < 0.001) and derived neutrophil-to-lymphocyte ratio (P = 0.035) were associated with poorer overall survival (OS). While these factors were nonsignificant in chemotherapy cohort. Three nomograms to predict response at 6-week after treatment, PFS and OS at 6-, 12- and 18-months were developed, and validated in validation cohort. The C-indices of each nomogram in both cohorts were as follow (training vs validation): 0.706 vs 0.701; 0.728 vs 0.701; 0.741 vs 0.709; respectively. AUC showed a good discriminative ability. Calibration curves demonstrated a consistence between actual results and predictions.

Conclusion

We developed predictive nomograms based on easily available factors to help clinicians early assess response and prognosis for NSCLC patients received anti-PD-1 antibody.

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
Fig. 7

Similar content being viewed by others

Data availability

All data is collected from medical record and is available.

Code availability

Not applicable.

Abbreviations

AEC:

Absolute eosinophil count

ALC:

Absolute lymphocyte count

ALK:

Anaplastic lymphoma kinase

AMC:

Absolute monocyte count

ANC:

Absolute neutrophil count

AUC:

Area under the curve

C-index:

Concordance index

CRP:

C-reactive protein

DDR:

DNA damage repair

dNLR:

Derived neutrophil-to-lymphocyte ratio

ECOG PS:

Eastern Cooperative Oncology Group performance status

EGFR:

Epidermal growth factor receptor

IDO:

Indoleamine2,3-dioxygenase

IQR:

Interquartile ranges

KRAS:

Kirsten rat sarcoma

LDH:

Lactate dehydrogenase

mOS:

Median overall survival

mPFS:

Median progression-free survival

MSI:

Microsatellite instability

NLR:

Neutrophil-to-lymphocyte ratio

NSCLC:

Non-small cell lung cancer

ORR:

Objective response rate

OS:

Overall survival

PD:

Progressive disease

PD-1:

Programmed death-1

PD-L1:

Programmed death-1-ligand 1

PFS:

Progression-free survival

PNI:

Prognostic nutritional index

PR:

Partial response

SD:

Stable disease

TILs:

Tumor infiltrating lymphocytes

TMB:

Tumor mutation burden

WBC:

White blood cell count

References

  1. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J (2016) Cancer statistics in China, 2015. CA Cancer J Clin 66:115–132. https://doi.org/10.3322/caac.21338

    Article  PubMed  Google Scholar 

  2. Travis WD, Brambilla E, Nicholson AG et al (2015) The 2015 World Health Organization Classification of Lung Tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol 10:1243–1260. https://doi.org/10.1097/JTO.0000000000000630

    Article  PubMed  Google Scholar 

  3. Brahmer J, Reckamp KL, Baas P et al (2015) Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med 373:123–135. https://doi.org/10.1056/NEJMoa1504627

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Borghaei H, Paz-Ares L, Horn L et al (2015) Nivolumab versus docetaxel in advanced non-squamous non-small-cell lung cancer. N Engl J Med 373:1627–1639. https://doi.org/10.1056/NEJMoa1507643

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Herbst RS, Baas P, Kim DW et al (2016) Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomized controlled trial. Lancet 387:1540–1550. https://doi.org/10.1016/S0140-6736(15)01281-7

    Article  CAS  PubMed  Google Scholar 

  6. Reck M, Rodriguez-Abreu D, Robinson AG et al (2016) Pembrolizumab versus chemotherapy for PD-L1–positive non–small-cell lung cancer. N Engl J Med 375:1823–1833. https://doi.org/10.1056/NEJMoa1606774

    Article  CAS  PubMed  Google Scholar 

  7. Hoy SM (2019) Sintilimab: first global approval. Drug 79:341–346. https://doi.org/10.1007/s40265-019-1066-z

    Article  CAS  Google Scholar 

  8. Keam SJ (2019) Toripalimab: first global approval. Drug 79:573–578. https://doi.org/10.1007/s40265-019-01076-2

    Article  CAS  Google Scholar 

  9. Markham A, Keam SJ (2019) Camrelizumab: first global approval. Drug 79:1355–1361. https://doi.org/10.1007/s40265-019-01167-0

    Article  Google Scholar 

  10. Lee A, Keam SJ (2020) Tislelizumab: first approval. Drug 80:617–624. https://doi.org/10.1007/s40265-020-01286-z

    Article  CAS  Google Scholar 

  11. Rizvi NA, Hellmann MD, Snyder A 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 

  12. Dudley JC, Lin MT, Le DT, Eshleman JR (2016) Microsatellite instability as a biomarker for PD-1 Blockade. Clin Cancer Res 22:813–820. https://doi.org/10.1158/1078-0432.CCR-15-1678

    Article  CAS  PubMed  Google Scholar 

  13. Conway JR, Kofman E, Mo SS, Elmarakeby H, Van Allen E (2018) Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Med 10:93. https://doi.org/10.1186/s13073-018-0605-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Botticelli A, Salati M, Di Pietro FR et al (2019) A nomogram to prepare survival in non-small cell lung cancer patients with nivolumab. J Transl Med 17:99. https://doi.org/10.1186/s12967-019-1847-x

    Article  PubMed  PubMed Central  Google Scholar 

  15. Park W, Kwon D, Saravia D et al (2018) Developing a predictive model for clinical outcomes of advanced non-small cell lung cancer patients treated with nivolumab. Clin Lung Cancer 19(280):288. https://doi.org/10.1016/j.cllc.2017.12.007

    Article  CAS  Google Scholar 

  16. Mo J, Hu X, Gu L, Chen B, Khadaroo PA, Shen Z, Dong L, Lv Y, Chitumba MN, Liu J (2020) Smokers or non-Smokers: who benefits more from immune checkpoint inhibitors in the treatment of malignancies? An up-to-date meta-analysis. World J Surg Oncol 18:15. https://doi.org/10.1186/s12957-020-1792-4

    Article  PubMed  PubMed Central  Google Scholar 

  17. Tanizaki J, Haratani K, Hayashi H et al (2018) Peripheral blood biomarkers associated with clinical outcome in non-small cell lung cancer patients treated with nivolumab. J Thorac Oncol 13:97–105. https://doi.org/10.1016/j.jtho.2017.10.030

    Article  CAS  PubMed  Google Scholar 

  18. Zhang ZB, Li Y, Yan X, Song Q, Wang GQ, Hu Y, Jiao SC, Wang JL (2019) Pretreatment lactate dehydrogenase may predict outcome of advanced non small-cell lung cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Cancer Med 8:1467–1473. https://doi.org/10.1002/cam4.2024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mezquita L, Auclin E, Ferrara R et al (2018) Association of the lung immune prognostic index with immune checkpoint inhibitor outcomes in patients with advanced non-small cell lung cancer. JAMA Oncol 4:351–357. https://doi.org/10.1001/jamaoncol.2017.4771

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shoji F, Takeoka H, Kozuma Y, Toyokawa G, Yamazaki K, Ichiki M, Takeo S (2019) Pretreatment prognostic nutritional index as will be a biomarker in non-small cell lung cancer patients treated with immune checkpoint inhibitors. Lung Cancer 136:45–51. https://doi.org/10.1016/j.lungcan.2019.08.006

    Article  PubMed  Google Scholar 

  21. Wang YZ, Li J, Xia Y et al (2013) Prognostic nomogram for intrahepatic cholangiocarcinoma after partial hepatectomy. J Clin Oncol 31:1188–1195. https://doi.org/10.1200/JCO.2012.41.5984

    Article  PubMed  Google Scholar 

  22. Goldstraw P, Crowley J, Chansky K, Giroux DJ, Groome PA, Rami-Porta R, Postmus PE, Rusch V, Sobin L, International Association for the Study of Lung Cancer International Staging Committee (2007) The IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the Forthcoming (Seventh) Edition of the TNM Classification of Malignant Tumours. J Thorac Oncol 2:706–714. https://doi.org/10.1097/JTO.0b013e31812f3c1a

    Article  PubMed  Google Scholar 

  23. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247. https://doi.org/10.1016/j.ejca.2008.10.026

    Article  CAS  PubMed  Google Scholar 

  24. Zhang LY, Sun LT, Yu JR et al (2019) Comparison of immune checkpoint inhibitors between older and younger patients with advanced or metastatic lung cancer: a systematic review and meta-analysis. Biomed Res Int 2019:9853701. https://doi.org/10.1155/2019/9853701

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Nishijima TF, Muss HB, Shachar SS, Moschos SJ (2016) Comparison of efficacy of immune checkpoint inhibitors (ICIs) between younger and older patients: a systematic review and meta-analysis. Cancer Treat Rev 45:30–37. https://doi.org/10.1016/j.ctrv.2016.02.006

    Article  CAS  PubMed  Google Scholar 

  26. Corbaux P, Maillet D, Boespflug A et al (2019) Older and younger patients treated with immune checkpoint inhibitors have similar outcomes in real-life setting. Eur J Cancer 121:192–201. https://doi.org/10.1016/j.ejca.2019.08.027

    Article  CAS  PubMed  Google Scholar 

  27. Wang S, Cowley LA, Liu XS (2019) Sex differences in cancer immunotherapy efficacy, biomarkers, and therapeutic strategy. Molecules 24:3214. https://doi.org/10.3390/molecules24183214

    Article  CAS  PubMed Central  Google Scholar 

  28. Alexandrov LB, Nik-Zainal S, Wedge DC et al (2013) Signatures of mutational processes in human cancer. Nature 500:415–421. https://doi.org/10.1038/nature12477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kerdidani D, Magkouta S, Chouvardas P et al (2018) Cigarette smoke-induced emphysema exhausts early cytotoxic CD8(+) T cell responses against nascent lung cancer cells. J Immunol 201:1558–1569. https://doi.org/10.4049/jimmunol.1700700

    Article  CAS  PubMed  Google Scholar 

  30. Herbst RS, Soria JC, Kowanetz M et al (2014) Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515:563–567. https://doi.org/10.1038/nature14011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Brahmer JR, Drake CG, Wollner I et al (2010) Phase I study of single-agent, anti-programmed death 1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol 28:3167–3175. https://doi.org/10.1200/JCO.2009.26.7609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Miao P, Sheng SL, Sun XG, Liu JJ, Huang G (2013) Lactate dehydrogenase A in Cancer: a promising target for diagnosis and therapy. IUBMB Life 65:904–910. https://doi.org/10.1002/iub.1216

    Article  CAS  PubMed  Google Scholar 

  33. Diakos CI, Charles KA, McMillan DC, Clarke SJ (2014) Cancer-related inflammation and treatment effectiveness. Lancet Oncol 15:e493–503. https://doi.org/10.1016/S1470-2045(14)70263-3

    Article  PubMed  Google Scholar 

  34. Proctor MJ, McMillan DC, Morrison DS, Fletcher CD, Horgan PG, Clarke SJ (2012) A derived neutrophil to lymphocyte ratio predicts survival in patients with cancer. Br J Cancer 107:695–699. https://doi.org/10.1038/bjc.2012.292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge Shaojun Xin, Fei Yu and Jiangnan Chen for their data collection. And we also would like to acknowledge Yanzhong Wang for proof reading the article.

Funding

Project supported by the National Natural Science Foundation of China (Grant No. 81972012). None of the funders had any role in the study design and the collection, analysis, and interpretation of data or in the writing of the article and the decision to submit it for publication. The researchers confirm their independence from funders and sponsors.

Author information

Authors and Affiliations

  1. Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun East Road, Jianggan District, Hangzhou, 310016, China

    Shijin Yuan, Yan Xia, Xinyou Xie & Jun Zhang

  2. Department of Clinical Laboratory, Jinhua Municipal Central Hospital, Jinhua, China

    Lihong Shen

  3. Department of Clinical Laboratory, Zhejiang Cancer Hospital, University of Chinese Academy of Science, Hangzhou, China

    Liuqing Ye

  4. Department of Clinical Laboratory, First People’s Hospital of Wenling, Wenzhou, China

    Lisha Li

  5. Department of Clinical Laboratory, Lishui City People’s Hospital, Lishui, China

    Lifen Chen

  6. Department of Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Haizhou Lou

Authors
  1. Shijin Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lihong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liuqing Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lisha Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lifen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinyou Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Haizhou Lou
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JZ, XX and HL conceived the idea, developed the theory, and JZ interpret the results. SY, LS, LY, LL and LC carried out the data collection. And JZ also supervised the data collection. YX conducted the statistical analysis. SY wrote the manuscript with support from YX. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Jun Zhang.

Ethics declarations

Conflict of interest

None of the authors have any disclosure to make.

Ethics approval

This work was carried out in human with approval from the Institutional Review Board. For this type of study, formal consent is not required. Institutional Review Board, therefore, granted waiver for this retrospective study.

Consent to participate

All authors consent to participate this study.

Consent for publication

All authors consent to publish this study.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 21 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, S., Xia, Y., Shen, L. et al. Development of nomograms to predict therapeutic response and prognosis of non-small cell lung cancer patients treated with anti-PD-1 antibody. Cancer Immunol Immunother 70, 533–546 (2021). https://doi.org/10.1007/s00262-020-02710-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00262-020-02710-9

Keywords

Search

Navigation