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Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics

  • Thoracic Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Objective

This study aimed to construct a new staging system for patients with esophageal squamous cell carcinoma (ESCC) based on combined pathological TNM (pTNM) stage, radiomics, and proteomics.

Methods

This study collected patients with radiomics and pTNM stage (Cohort 1, n = 786), among whom 103 patients also had proteomic data (Cohort 2, n = 103). The Cox regression model with the least absolute shrinkage and selection operator, and the Cox proportional hazards model were used to construct a nomogram and predictive models. Concordance index (C-index) and the integrated area under the time-dependent receiver operating characteristic (ROC) curve (IAUC) were used to evaluate the predictive models. The corresponding staging systems were further assessed using Kaplan–Meier survival curves.

Results

For Cohort 1, the RadpTNM4c staging systems, constructed based on combined pTNM stage and radiomic features, outperformed the pTNM4c stage in both the training dataset 1 (Train1; IAUC 0.711 vs. 0.706, p < 0.001) and the validation dataset 1 (Valid1; IAUC 0.695 vs. 0.659, p < 0.001; C-index 0.703 vs. 0.674, p = 0.029). For Cohort 2, the ProtRadpTNM2c staging system, constructed based on combined pTNM stage, radiomics, and proteomics, outperformed the pTNM2c stage in both the Train2 (IAUC 0.777 vs. 0.610, p < 0.001; C-index 0.898 vs. 0.608, p < 0.001) and Valid2 (IAUC 0.746 vs. 0.608, p < 0.001; C-index 0.889 vs. 0.641, p = 0.009) datasets.

Conclusions

The ProtRadpTNM2c staging system, based on combined pTNM stage, radiomic, and proteomic features, improves the predictive performance of the classical pTNM staging system.

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References

  1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.

    Article  PubMed  Google Scholar 

  2. Smyth EC, Lagergren J, Fitzgerald RC, et al. Oesophageal cancer. Nat Rev Dis Primers. 2017;3:17048. https://doi.org/10.1038/nrdp.2017.48.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Sun HB, Li Y, Liu XB, et al. Early oral feeding following mckeown minimally invasive esophagectomy: an open-label, randomized, controlled, noninferiority trial. Ann Surg. 2018;267(3):435–42. https://doi.org/10.1097/sla.0000000000002304.

    Article  PubMed  Google Scholar 

  4. Sonohara F, Gao F, Iwata N, et al. Genome-wide discovery of a novel gene-expression signature for the identification of lymph node metastasis in esophageal squamous cell carcinoma. Ann Surg. 2019;269(5):879–86. https://doi.org/10.1097/SLA.0000000000002622.

    Article  PubMed  Google Scholar 

  5. Pennathur A, Gibson MK, Jobe BA, Luketich JD. Oesophageal carcinoma. The Lancet. 2013;381(9864):400–12. https://doi.org/10.1016/s0140-6736(12)60643-6.

    Article  Google Scholar 

  6. Chang C, Zhou S, Yu H, et al. A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma. Eur Radiol. 2021;31(8):6259–68. https://doi.org/10.1007/s00330-020-07676-x.

    Article  CAS  PubMed  Google Scholar 

  7. Rice TW, Ishwaran H, Ferguson MK, Blackstone EH, Goldstraw P. Cancer of the esophagus and esophagogastric junction: an eighth edition staging primer. J Thorac Oncol. 2017;12(1):36–42. https://doi.org/10.1016/j.jtho.2016.10.016.

    Article  PubMed  Google Scholar 

  8. Semenkovich TR, Yan Y, Subramanian M, et al. A clinical nomogram for predicting node-positive disease in esophageal cancer. Ann Surg. 2021;273(6):e214–21. https://doi.org/10.1097/sla.0000000000003450.

    Article  PubMed  Google Scholar 

  9. Luo HS, Chen YY, Huang WZ, et al. Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer. Radiat Oncol. 2021;16(1):201. https://doi.org/10.1186/s13014-021-01925-z.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kim Y, Margonis GA, Prescott JD, et al. Nomograms to predict recurrence-free and overall survival after curative resection of adrenocortical carcinoma. JAMA Surg. 2016;151(4):365–73. https://doi.org/10.1001/jamasurg.2015.4516.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Mabuchi S, Komura N, Sasano T, et al. Pretreatment tumor-related leukocytosis misleads positron emission tomography-computed tomography during lymph node staging in gynecological malignancies. Nat Commun. 2020;11(1):1364. https://doi.org/10.1038/s41467-020-15186-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zaidi MY, Lopez-Aguiar AG, Dillhoff M, et al. Prognostic role of lymph node positivity and number of lymph nodes needed for accurately staging small-bowel neuroendocrine tumors. JAMA Surg. 2019;154(2):134–40. https://doi.org/10.1001/jamasurg.2018.3865.

    Article  PubMed  Google Scholar 

  13. Deng W, Xu T, Xu Y, et al. Survival patterns for patients with resected N2 non-small cell lung cancer and postoperative radiotherapy: a prognostic scoring model and heat map approach. J Thorac Oncol. 2018;13(12):1968–74. https://doi.org/10.1016/j.jtho.2018.08.2021.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tan H, Zhang H, Xie J, et al. A novel staging model to classify oesophageal squamous cell carcinoma patients in China. Br J Cancer. 2014;110(8):2109–15. https://doi.org/10.1038/bjc.2014.101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Liu W, Xie L, He YH, et al. Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting. Nat Commun. 2021;12(1):4961. https://doi.org/10.1038/s41467-021-25202-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu W, He JZ, Wang SH, et al. MASAN: a novel staging system for prognosis of patients with oesophageal squamous cell carcinoma. Br J Cancer. 2018;118(11):1476–84. https://doi.org/10.1038/s41416-018-0094-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Shang QX, Yang YS, Xu LY, et al. Prognostic role of nodal skip metastasis in thoracic esophageal squamous cell carcinoma: a large-scale multicenter study. Ann Surg Oncol. 2021;28(11):6341–52. https://doi.org/10.1245/s10434-020-09509-z.

    Article  PubMed  Google Scholar 

  18. James P. Protein identification in the post-genome era: the rapid rise of proteomics. Q Rev Biophys. 1997;30(4):279–331. https://doi.org/10.1017/s0033583597003399.

    Article  CAS  PubMed  Google Scholar 

  19. Lau E, Venkatraman V, Thomas CT, Wu JC, Van Eyk JE, Lam MPY. Identifying high-priority proteins across the human diseasome using semantic similarity. J Proteome Res. 2018;17(12):4267–78. https://doi.org/10.1021/acs.jproteome.8b00393.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006.

    Article  CAS  PubMed  Google Scholar 

  21. Cotton S, Ferreira D, Soares J, et al. Target score-a proteomics data selection tool applied to esophageal cancer identifies GLUT1-Sialyl Tn glycoforms as biomarkers of cancer aggressiveness. Int J Mol Sci. 2021;22(4):1664. https://doi.org/10.3390/ijms22041664.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Guan Z, Wang Y, Wang Y, et al. Long non-coding RNA LOC100133669 promotes cell proliferation in oesophageal squamous cell carcinoma. Cell Proliferation. 2020;53(4):e12750. https://doi.org/10.1111/cpr.12750.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Jin X, Liu L, Wu J, et al. A multi-omics study delineates new molecular features and therapeutic targets for esophageal squamous cell carcinoma. Clin Transl Med. 2021;11(9):e538. https://doi.org/10.1002/ctm2.538.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liu L, Wu J, Shi M, et al. New metabolic alterations and predictive marker pipecolic acid in sera for esophageal squamous cell carcinoma. Genomics, Proteomics & Bioinformatics. Epub 26 Mar 2022. https://doi.org/10.1016/j.gpb.2021.08.016.

  25. Wang Y, Zhang W, Liu W, et al. Long noncoding RNA VESTAR regulates lymphangiogenesis and lymph node metastasis of esophageal squamous cell carcinoma by enhancing VEGFC mRNA stability. Cancer Res. 2021;81(12):3187–99. https://doi.org/10.1158/0008-5472.Can-20-1713.

    Article  CAS  PubMed  Google Scholar 

  26. Wu Q, Liu F, Ge M, et al. BRD4 drives esophageal squamous cell carcinoma growth by promoting RCC2 expression. Oncogene. 2022;41(3):347–60. https://doi.org/10.1038/s41388-021-02099-4.

    Article  CAS  PubMed  Google Scholar 

  27. Zhang L, Gao Y, Zhang X, et al. TSTA3 facilitates esophageal squamous cell carcinoma progression through regulating fucosylation of LAMP2 and ERBB2. Theranostics. 2020;10(24):11339–58. https://doi.org/10.7150/thno.48225.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhang Y, Chen Y. Stratification from heterogeneity of the cell-death signal enables prognosis prediction and immune microenvironment characterization in esophageal squamous cell carcinoma. Front Cell Dev Biol. 2022;10:855404. https://doi.org/10.3389/fcell.2022.855404.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Li Y, Beck M, Päßler T, et al. A FDG-PET radiomics signature detects esophageal squamous cell carcinoma patients who do not benefit from chemoradiation. Sci Rep. 2020;10(1):17671. https://doi.org/10.1038/s41598-020-74701-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lu N, Zhang WJ, Dong L, et al. Dual-region radiomics signature: Integrating primary tumor and lymph node computed tomography features improves survival prediction in esophageal squamous cell cancer. Comput Methods Programs Biomed. 2021;208:106287. https://doi.org/10.1016/j.cmpb.2021.106287.

    Article  PubMed  Google Scholar 

  31. Peng H, Xue T, Chen Q, Li M, Ge Y, Feng F. Computed tomography-based radiomics nomogram for predicting the postoperative prognosis of esophageal squamous cell carcinoma: a multicenter study. Acad Radiol. 2022. https://doi.org/10.1016/j.acra.2022.01.020.

    Article  PubMed  Google Scholar 

  32. Tan X, Ma Z, Yan L, Ye W, Liu Z, Liang C. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol. 2019;29(1):392–400. https://doi.org/10.1007/s00330-018-5581-1.

    Article  PubMed  Google Scholar 

  33. Xie CY, Hu YH, Ho JW, et al. Using genomics feature selection method in radiomics pipeline improves prognostication performance in locally advanced esophageal squamous cell carcinoma—a pilot study. Cancers. 2021;13(9):2145. https://doi.org/10.3390/cancers13092145.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Rice TW, Gress DM, Patil DT, Hofstetter WL, Kelsen DP, Blackstone EH. Cancer of the esophagus and esophagogastric junction-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin. 2017;67(4):304–17. https://doi.org/10.3322/caac.21399.

    Article  PubMed  Google Scholar 

  35. Ligero M, Garcia-Ruiz A, Viaplana C, et al. A CT-based radiomics signature is associated with response to immune checkpoint inhibitors in advanced solid tumors. Radiology. 2021;299(1):109–19. https://doi.org/10.1148/radiol.2021200928.

    Article  PubMed  Google Scholar 

  36. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323–41. https://doi.org/10.1016/j.mri.2012.05.001.

    Article  PubMed  PubMed Central  Google Scholar 

  37. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7. https://doi.org/10.1158/0008-5472.CAN-17-0339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–38. https://doi.org/10.1148/radiol.2020191145.

    Article  PubMed  Google Scholar 

  39. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87.

    Article  PubMed  Google Scholar 

  40. Haibe-Kains B, Desmedt C, Sotiriou C, Bontempi G. A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics. 2008;24(19):2200–8. https://doi.org/10.1093/bioinformatics/btn374.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kamarudin AN, Cox T, Kolamunnage-Dona R. Time-dependent ROC curve analysis in medical research: current methods and applications. BMC Med Res Methodol. 2017;17(1):53. https://doi.org/10.1186/s12874-017-0332-6.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lim SB, Tan SJ, Lim WT, Lim CT. An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer. Nat Commun. 2017;8(1):1734. https://doi.org/10.1038/s41467-017-01430-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lin DC, Wang MR, Koeffler HP. Genomic and epigenomic aberrations in esophageal squamous cell carcinoma and implications for patients. Gastroenterology. 2018;154(2):374–89. https://doi.org/10.1053/j.gastro.2017.06.066.

    Article  PubMed  Google Scholar 

  44. Akhtar J, Wang Z, Yu C, Zhang ZP, Bi MM. STMN-1 gene: a predictor of survival in stage iia esophageal squamous cell carcinoma after Ivor-Lewis esophagectomy? Ann Surg Oncol. 2014;21(1):315–21. https://doi.org/10.1245/s10434-013-3215-z.

    Article  PubMed  Google Scholar 

  45. Duan J, Xie Y, Qu L, 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(1):100. https://doi.org/10.1186/s40425-018-0418-7.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Araujo-Filho JAB, Mayoral M, Zheng J, et al. CT radiomic features for predicting resectability and TNM staging in thymic epithelial tumors. Ann Thoracic Surg. 2022;113(3):957–65. https://doi.org/10.1016/j.athoracsur.2021.03.084.

    Article  Google Scholar 

  47. Demirjian NL, Varghese BA, Cen SY, et al. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. Eur Radiol. 2022;32(4):2552–63. https://doi.org/10.1007/s00330-021-08344-4.

    Article  PubMed  Google Scholar 

  48. Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, Fabro AT, Azevedo-Marques PM. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed. 2018;159:23–30. https://doi.org/10.1016/j.cmpb.2018.02.015.

    Article  PubMed  Google Scholar 

  49. Hussain MA, Hamarneh G, Garbi R. Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging. Comput Med Imaging Graph. 2021;90:101924. https://doi.org/10.1016/j.compmedimag.2021.101924.

    Article  PubMed  Google Scholar 

  50. Wang J, Tang S, Mao Y, et al. Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker. Hepatol Int. 2022;16(3):627–39. https://doi.org/10.1007/s12072-022-10326-7.

    Article  PubMed  Google Scholar 

  51. Xie T, Wang X, Li M, Tong T, Yu X, Zhou Z. Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection. Eur Radiol. 2020;30(5):2513–24. https://doi.org/10.1007/s00330-019-06600-2.

    Article  PubMed  Google Scholar 

  52. Yang M, Hu P, Li M, et al. Computed tomography-based radiomics in predicting T stage and length of esophageal squamous cell carcinoma. Front Oncol. 2021;11:722961. https://doi.org/10.3389/fonc.2021.722961.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wu L, Wang C, Tan X, et al. Radiomics approach for preoperative identification of stages I-II and III-IV of esophageal cancer. Chin J Cancer Res. 2018;30(4):396–405. https://doi.org/10.21147/j.issn.1000-9604.2018.04.02.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Del Carmen S, Corchete LA, Gervas R, et al. Prognostic implications of EGFR protein expression in sporadic colorectal tumors: Correlation with copy number status, mRNA levels and miRNA regulation. Sci Rep. 2020;10(1):4662. https://doi.org/10.1038/s41598-020-61688-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bian SB, Yang Y, Liang WQ, Zhang KC, Chen L, Zhang ZT. Leukemia inhibitory factor promotes gastric cancer cell proliferation, migration, and invasion via the LIFR-Hippo-YAP pathway. Ann N Y Acad Sci. 2021;1484(1):74–89. https://doi.org/10.1111/nyas.14466.

    Article  CAS  PubMed  Google Scholar 

  56. Granata V, Fusco R, De Muzio F, et al. Contrast MR-based radiomics and machine learning analysis to assess clinical outcomes following liver resection in colorectal liver metastases: a preliminary study. Cancers. 2022;14(5):1110. https://doi.org/10.3390/cancers14051110.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Deng J, Chen H, Zhou D, et al. Comparative genomic analysis of esophageal squamous cell carcinoma between Asian and Caucasian patient populations. Nat Commun. 2017;8(1):1533. https://doi.org/10.1038/s41467-017-01730-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhang J, Jiang Y, Wu C, et al. Comparison of clinicopathologic features and survival between eastern and western population with esophageal squamous cell carcinoma. J Thorac Dis. 2015;7(10):1780–6. https://doi.org/10.3978/j.issn.2072-1439.2015.10.39.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgment

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 82272945), the Science and Technology Special Fund of Guangdong Province of China (Grant Nos. 210713176903543 and 210729156901797), the Innovative Team Grant of Guangdong Department of Education (2021KCXTD005), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant of Hong Kong (Grant No. 2020LKSFG07B), and the Natural Science Foundation of Heilongjiang Province (Grant No. LH2021F048).

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Author notes

  1. Shao-Jun Zheng, Chun-Peng Zheng, and Tian-Tian Zhai contributed equally to this work.

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, 515041, Guangdong, China

    Shao-Jun Zheng MM, Xiu-E Xu BM, Ya-Qi Zheng MM, Zhi-Mao Li MM & Li-Yan Xu PhD

  2. Department of Surgical Oncology, Shantou Central Hospital, Shantou, 515041, Guangdong, China

    Shao-Jun Zheng MM & Chun-Peng Zheng MM

  3. Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515041, Guangdong, China

    Tian-Tian Zhai MD

  4. The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, 515041, Guangdong, China

    En-Min Li PhD

  5. College of Science, Heilongjiang Institute of Technology, Harbin, Heilongjiang, China

    Wei Liu PhD

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Shao-Jun Zheng, Chun-Peng Zheng, Tian-Tian Zhai, Xiu-E Xu, Ya-Qi Zheng, Zhi-Mao Li, En-Min Li, Wei Liu, and Li-Yan Xu declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Zheng, SJ., Zheng, CP., Zhai, TT. et al. Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics. Ann Surg Oncol 30, 2227–2241 (2023). https://doi.org/10.1245/s10434-022-13026-6

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