AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Cancer Biology & Medicine Article
PDF (1.1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Original Article | Open Access

A nomogram-based immune-serum scoring system predicts overall survival in patients with lung adenocarcinoma

Qiujuan Huang1, Tongyuan Qu1, Lisha Qi1, Changxu Liu2, Yuhong Guo1, Qianru Guo1, Guangning Li1, Yalei Wang1, Wenshuai Zhang1, Wei Zhao1, Danyang Ren1, Leina Sun1, Shengguang Wang3, Bin Meng1, Baocun Sun1, Bin Zhang4, Wenjuan Ma5 ( ), Wenfeng Cao1 ( )
Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
Department of Pathology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin 300120, China
Department of Lung Cancer, Tianjin Lung Cancer Center
Department of Breast Cancer
Department of Breast Imaging, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
Show Author Information

Abstract

Objective

The immunoscore, which is used to quantify immune infiltrates, has greater relative prognostic value than tumor, node, and metastasis (TNM) stage and might serve as a new system for classification of colorectal cancer. However, a comparable immunoscore for predicting lung adenocarcinoma (LUAD) prognosis is currently lacking.

Methods

We analyzed the expression of 18 immune features by immunohistochemistry in 171 specimens. The relationship of immune marker expression and clinicopathologic factors to the overall survival (OS) was analyzed with the Kaplan-Meier method. A nomogram was developed by using the optimal features selected by least absolute shrinkage and selection operator (LASSO) regression in the training cohort (n = 111) and evaluated in the validation cohort (n = 60).

Results

The indicators integrated in the nomogram were TNM stage, neuron-specific enolase, carcino-embryonic antigen, CD8center of tumor (CT), CD8invasive margin (IM), FoxP3CT, and CD45ROCT. The calibration curve showed prominent agreement between the observed 2- and 5-year OS and that predicted by the nomogram. To simplify the nomogram, we developed a new immune-serum scoring system (Ⅰ-SSS) based on the points awarded for each factor in the nomogram. Our Ⅰ-SSS was able to stratify same-stage patients into different risk subgroups. The combination of Ⅰ-SSS and TNM stage had better prognostic value than the TNM stage alone.

Conclusions

Our new Ⅰ-SSS can accurately and individually predict LUAD prognosis and may be used to supplement prognostication based on the TNM stage.

Keywords

prognosisnomogramLung adenocarcinomaimmune microenvironmentimmunoscore

Electronic Supplementary Material

Download File(s)
cbm-18-2-517_ESM.pdf (277.6 KB)

References

1

Galon J, Pages F, Marincola FM, Angell HK, Thurin M, Lugli A, et al. Cancer classification using the immunoscore: a worldwide task force. J Transl Med. 2012; 10: 205.
Crossref Google Scholar
2

Donnem T, Kilvaer TK, Andersen S, Richardsen E, Paulsen EE, Hald SM, et al. Strategies for clinical implementation of TNM-immunoscore in resected nonsmall-cell lung cancer. Ann Oncol. 2016; 27: 225-32.
Crossref Google Scholar
3

Liang W, Zhang L, Jiang G, Wang Q, Liu L, Liu D, et al. Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer. J Clin Oncol. 2015; 33: 861-9.
Crossref Google Scholar
4

Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012; 21: 309-22.
Crossref Google Scholar
5

Yuan Y, Jiang YC, Sun CK, Chen QM. Role of the tumor microenvironment in tumor progression and the clinical applications (Review). Oncol Rep. 2016; 35: 2499-515.
Crossref Google Scholar
6

Broussard EK, Disis ML. TNM staging in colorectal cancer: T is for T cell and m is for memory. J Clin Oncol. 2011; 29: 601-3.
Crossref Google Scholar
7

Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, et al. Towards the introduction of the ‘immunoscore’ in the classification of malignant tumours. J Pathol. 2014; 232: 199-209.
Crossref Google Scholar
8

Angell H, Galon J. From the immune contexture to the immunoscore: the role of prognostic and predictive immune markers in cancer. Curr Opin Immunol. 2013; 25: 261-7.
Crossref Google Scholar
9

Wirta EV, Seppala T, Friman M, Vayrynen J, Ahtiainen M, Kautiainen H, et al. Immunoscore in mismatch repair-proficient and -deficient colon cancer. J Pathol Clin Res. 2017; 3: 203-13.
Crossref Google Scholar
10

Waldmann P, Ferencakovic M, Meszaros G, Khayatzadeh N, Curik I, Solkner J. AUTALASSO: an automatic adaptive lasso for genome-wide prediction. BMC Bioinformatics. 2019; 20: 167.
Crossref Google Scholar
11

Lee CK, Asher R, Friedlander M, Gebski V, Gonzalez-Martin A, Lortholary A, et al. Development and validation of a prognostic nomogram for overall survival in patients with platinum-resistant ovarian cancer treated with chemotherapy. Eur J Cancer. 2019; 117: 99-106.
Crossref Google Scholar
12

Zhu C, Zou C, Guan G, Guo Q, Yan Z, Liu T, et al. Development and validation of an interferon signature predicting prognosis and treatment response for glioblastoma. Oncoimmunology. 2019; 8: e1621677.
Crossref Google Scholar
13

Duan J, Xie Y, Qu L, Wang L, Zhou S, Wang Y, et al. A nomogram-based immunoprofile predicts overall survival for previously untreated patients with esophageal squamous cell carcinoma after esophagectomy. J Immunother Cancer. 2018; 6: 100.
Crossref Google Scholar
14

Liu F, Zhou R, Jiang F, Liu G, Li K, Zhu G. Proposal of a nomogram for predicting survival in patients with Siewert Type Ⅱ adenocarcinoma of the esophagogastric junction after preoperative radiation. Ann Surg Oncol. 2019; 26: 1292-300.
Crossref Google Scholar
15

Liu F, Lang R, Zhao J, Zhang X, Pringle GA, Fan Y, et al. CD8(+) cytotoxic T cell and FOXP3(+) regulatory T cell infiltration in relation to breast cancer survival and molecular subtypes. Breast Cancer Res Treat. 2011; 130: 645-55.
Crossref Google Scholar
16

Dobrovolskiene NT, Cicenas S, Kazlauskaite N, Miseikyte-Kaubriene E, Krasko JA, Ostapenko V, et al. CD8(high)CD57(+) T-cell population as an independent predictor of response to chemoradiation therapy in extensive-stage small cell lung cancer. Lung Cancer. 2015; 90: 326-33.
Crossref Google Scholar
17

Paulsen EE, Kilvaer T, Khanehkenari MR, Maurseth RJ, Al-Saad S, Hald SM, et al. CD45RO(+) memory T lymphocytes – a candidate marker for TNM-immunoscore in squamous non-small cell lung cancer. Neoplasia. 2015; 17: 839-48.
Crossref Google Scholar
18

Sinicrope FA, Rego RL, Ansell SM, Knutson KL, Foster NR, Sargent DJ. Intraepithelial effector (CD3+)/regulatory (FoxP3+) T-cell ratio predicts a clinical outcome of human colon carcinoma. Gastroenterology. 2009; 137: 1270-9.
Crossref Google Scholar
19

Galdiero MR, Bianchi P, Grizzi F, Di Caro G, Basso G, Ponzetta A, et al. Occurrence and significance of tumor-associated neutrophils in patients with colorectal cancer. Int J Cancer. 2016; 139: 446-56.
Crossref Google Scholar
20

Ishigami S, Natsugoe S, Tokuda K, Nakajo A, Che X, Iwashige H, et al. Prognostic value of intratumoral natural killer cells in gastric carcinoma. Cancer. 2000; 88: 577-83.
Crossref Google Scholar
21

Ilie M, Hofman V, Ortholan C, Bonnetaud C, Coelle C, Mouroux J, et al. Predictive clinical outcome of the intratumoral CD66b-positive neutrophil-to-CD8-positive T-cell ratio in patients with resectable nonsmall cell lung cancer. Cancer. 2012; 118: 1726-37.
Crossref Google Scholar
22

Gong Z, Chen J, Cheng JN, Sun C, Jia Q, Diao X, et al. Tumor microenvironment properties are associated with low CD68-positive cell infiltration and favorable disease-free survival in EGFR-mutant lung adenocarcinoma. Clin Lung Cancer. 2018; 19: e551-8.
Crossref Google Scholar
23

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 β2 (IL-12Rβ2), IL-7R, and stromal FoxP3/CD3 ratio are independent predictors of recurrence. J Clin Oncol. 2013; 31: 490-8.
Crossref Google Scholar
24

Kuwahara T, Hazama S, Suzuki N, Yoshida S, Tomochika S, Nakagami Y, et al. Intratumoural-infiltrating CD4 + and FOXP3 + T cells as strong positive predictive markers for the prognosis of resectable colorectal cancer. Br J Cancer. 2019; 121: 659-65.
Crossref Google Scholar
25

Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006; 313: 1960-4.
Crossref Google Scholar
26

Zhang JX, Song W, Chen ZH, Wei JH, Liao YJ, Lei J, et al. Prognostic and predictive value of a microrna signature in stage ii colon cancer: a microRNA expression analysis. Lancet Oncol. 2013; 14: 1295-306.
Crossref Google Scholar
27

Wang J, Jiang W, Zhang T, Liu L, Bi N, Wang X, et al. Increased CYFRA 21-1, CEA and NSE are prognostic of poor outcome for locally advanced squamous cell carcinoma in lung: a nomogram and recursive partitioning risk stratification analysis. Transl Oncol. 2018; 11: 999-1006.
Crossref Google Scholar
28

Li S, Cao L, Wang X, Wang F, Wang L, Jiang R. Neuron-specific enolase is an independent prognostic factor in resected lung adenocarcinoma patients with anaplastic lymphoma kinase gene rearrangements. Med Sci Monit. 2019; 25: 675-90.
Crossref Google Scholar
29

Fridman WH, Pages F, Sautes-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012; 12: 298-306.
Crossref Google Scholar
30

Kondratiev S, Sabo E, Yakirevich E, Lavie O, Resnick MB. Intratumoral CD8+ T lymphocytes as a prognostic factor of survival in endometrial carcinoma. Clin Cancer Res. 2004; 10: 4450-6.
Crossref Google Scholar
31

Donnem T, Hald SM, Paulsen EE, Richardsen E, Al-Saad S, Kilvaer TK, et al. Stromal cd8+ t-cell density-a promising supplement to TNM staging in non-small cell lung cancer. Clin Cancer Res. 2015; 21: 2635-43.
Crossref Google Scholar
32

Shang B, Liu Y, Jiang SJ, Liu Y. Prognostic value of tumor-infiltrating FoxP3+ regulatory t cells in cancers: a systematic review and meta-analysis. Sci Rep. 2015; 5: 15179.
Crossref Google Scholar
33

Kadota K, Nitadori JI, Adusumilli PS. Prognostic value of the immune microenvironment in lung adenocarcinoma. Oncoimmunology. 2013; 2: e24036.
Crossref Google Scholar
34

Wculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D. Dendritic cells in cancer immunology and immunotherapy. Nat Rev Immunol. 2020; 20: 7-24.
Crossref Google Scholar
35

Menetrier-Caux C, Gobert M, Caux C. Differences in tumor regulatory t-cell localization and activation status impact patient outcome. Cancer Res. 2009; 69: 7895-8.
Crossref Google Scholar
36

Kryczek I, Grybos M, Dlubek D, Klimczak A, Rabczynski J, Lange A. Accumulation of CD45RO(+) cells in peritoneal carcinomatous fluid favours survival of ovarian carcinoma patients. Cancer Immunol Immunother. 2002; 51: 513-9.
Crossref Google Scholar
37

Wang W, Hodkinson P, McLaren F, Mackean MJ, Williams L, Howie SEM, et al. Histologic assessment of tumor-associated cd45(+) cell numbers is an independent predictor of prognosis in small cell lung cancer. Chest. 2013; 143: 146-51.
Crossref Google Scholar
38

Hu Z, Gu X, Zhong R, Zhong H. Tumor-infiltrating CD45RO(+) memory cells correlate with favorable prognosis in patients with lung adenocarcinoma. J Thorac Dis. 2018; 10: 2089-99.
Crossref Google Scholar
39

Gao J, Ren Y, Guo H, Mao R, Xie H, Su H, et al. A new method for predicting survival in stage Ⅰ non-small cell lung cancer patients: nomogram based on macrophage immunoscore, TNM stage and lymphocyte-to-monocyte ratio. Ann Transl Med. 2020; 8: 470.
Crossref Google Scholar
40

Pages F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C, et al. International validation of the consensus immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet (London, England) 2018; 391: 2128-39.
Crossref Google Scholar
41

Pages F, Galon J, Dieu-Nosjean MC, Tartour E, Sautes-Fridman C, Fridman WH. Immune infiltration in human tumors: a prognostic factor that should not be ignored. Oncogene. 2010; 29: 1093-102.
Crossref Google Scholar
42

Mlecnik B, Bindea G, Pages F, Galon J. Tumor immunosurveillance in human cancers. Cancer Metastasis Rev. 2011; 30: 5-12.
Crossref Google Scholar
43

Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity. 2013; 39: 11-26.
Crossref Google Scholar
44

Kirilovsky A, Marliot F, El Sissy C, Haicheur N, Galon J, Pages F. Rational bases for the use of the immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients. Int Immunol. 2016; 28: 373-82.
Crossref Google Scholar
Cancer Biology & Medicine
Volume 18 Issue 2,
May 2021
Pages 517-529
DOI: 10.20892/j.issn.2095-3941.2020.0648
Cite this article:
Huang Q, Qu T, Qi L, et al. A nomogram-based immune-serum scoring system predicts overall survival in patients with lung adenocarcinoma. Cancer Biology & Medicine, 2021, 18(2): 517-529. https://doi.org/10.20892/j.issn.2095-3941.2020.0648

66

Views

0

Downloads

8

Crossref

6

Web of Science

7

Scopus

Altmetrics
Received: 21 October 2020
Accepted: 31 December 2020
Published: 01 May 2021
©2021 Cancer Biology & Medicine.

Creative Commons Attribution-NonCommercial 4.0 International License
Return