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Updated: May 4, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Prediction of survival in resected non-small cell lung cancer using a protein expression-based risk model:
Kathryn A Gold1, Edward S Kim, Diane D Liu
1Authors' Affiliations: Departments of Thoracic/Head and Neck Medical Oncology, Biostatistics, Pathology, and Thoracic and Cardiovascular Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas; Levine Cancer Center; Philips Digital Pathology; and Department of Pathology, University of San Francisco Xavier de Chuquisaca Sucre-Bolivia, San Francisco, California.
Purpose:
Patients with resected non-small cell lung cancer (NSCLC) are at risk for recurrence of disease, but we do not have tools to predict which patients are at highest risk. We set out to create a risk model incorporating both clinical data and biomarkers.
Experimental Design:
We assembled a comprehensive database with archival tissues and clinical follow-up from patients with NSCLC resected between 2002 and 2005. Twenty-one proteins identified from our preclinical studies as related to lung carcinogenesis were investigated, including pathways related to metabolism, DNA repair, inflammation, and growth factors. Expression of proteins was quantified using immunohistochemistry. Immunohistochemistry was chosen because it is widely available and can be performed on formalin-fixed paraffin-embedded specimens. Cox models were fitted to estimate effects of clinical factors and biomarkers on recurrence-free survival (RFS) and overall survival (OS).
Results:
A total of 370 patients are included in our analysis. With median follow-up of 5.3 years, median OS is 6.4 years. A total of 209 cases with recurrence or death were observed. Multicovariate risk models for RFS and OS were developed including relevant biomarkers, age, and stage. Increased expression of phospho-adenosine monophosphate-activated protein kinase (pAMPK), phospho-mTOR (pmTOR), epithelial cell adhesion molecule (EpCAM), and calcium/calmodulin-dependent serine protein kinase were significant (P < 0.05) predictors for favorable RFS; insulin receptor, chemokine (C-X-C motif) receptor 2 (CXCR2), and insulin-like growth factor-1 receptor predicted for unfavorable RFS. Significant (P < 0.05) predictors for favorable OS include pAMPK, pmTOR, and EpCAM; CXCR2 and flap structure-specific endonuclease-1 predicted unfavorable OS.
Conclusion:
We have developed a comprehensive risk model predictive for recurrence in our large retrospective database, which is one of the largest reported series of resected NSCLC.
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