Transcriptomics in Tumor and Normal Lung Tissues Identify Patients With Early-Stage Non–Small-Cell Lung Cancer With High Risk of Postsurgery Recurrence Who May Benefit From Adjuvant Therapies

PURPOSE The prognosis of patients with non–small-cell lung cancer (NSCLC), traditionally determined by anatomic histology and TNM staging, neglects the biological features of the tumor that may be important in determining patient outcome and guiding therapeutic interventions. Identifying patients with NSCLC at increased risk of recurrence after curative-intent surgery remains an important unmet need so that known effective adjuvant treatments can be offered to those at highest risk of recurrence. METHODS Relative gene expression level in the primary tumor and normal bronchial tissues was used to retrospectively assess their association with disease-free survival (DFS) in a cohort of 120 patients with NSCLC who underwent curative-intent surgery. RESULTS Low versus high Digital Display Precision Predictor (DDPP) score (a measure of relative gene expression) was significantly associated with shorter DFS (highest recurrence risk; P = .006) in all patients and in patients with TNM stages 1-2 (P = .00051; n = 83). For patients with stages 1-2 and low DDPP score (n = 29), adjuvant chemotherapy was associated with improved DFS (P = .0041). High co-overexpression of CTLA-4, PD-L1, and ICOS in normal lung (28 of 120 patients) was also significantly associated with decreased DFS (P = .0013), suggesting an immune tolerance to tumor neoantigens in some patients. Patients with DDPP low and immunotolerant normal tissue had the shortest DFS (P = 2.12E–11). CONCLUSION TNM stage, DDPP score, and immune competence status of normal lung are independent prognostic factors in multivariate analysis. Our findings open new avenues for prospective prognostic assessment and treatment assignment on the basis of transcriptomic profiling of tumor and normal lung tissue in patients with NSCLC.

[1]  N. Girard,et al.  Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival , 2021, npj Precision Oncology.

[2]  P. Sharma,et al.  The Next Decade of Immune Checkpoint Therapy. , 2021, Cancer discovery.

[3]  M. Lenardo,et al.  A guide to cancer immunotherapy: from T cell basic science to clinical practice , 2020, Nature Reviews Immunology.

[4]  H. Groen,et al.  Analysis of Released Circulating Tumor Cells During Surgery for Non-Small Cell Lung Cancer , 2019, Clinical Cancer Research.

[5]  K. Syrigos,et al.  Nivolumab plus Ipilimumab in Advanced Non-Small-Cell Lung Cancer. , 2019, The New England journal of medicine.

[6]  Virginia G Kaklamani,et al.  Adjuvant Chemotherapy Guided by a 21‐Gene Expression Assay in Breast Cancer , 2018, The New England journal of medicine.

[7]  P. Jänne,et al.  Pooled Analysis of the Prognostic and Predictive Effects of TP53 Comutation Status Combined With KRAS or EGFR Mutation in Early-Stage Resected Non-Small-Cell Lung Cancer in Four Trials of Adjuvant Chemotherapy. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  J. Crowley,et al.  The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[9]  J. Austin,et al.  The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. , 2015, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[10]  J. Hernando Cubero,et al.  Adjuvant chemotherapy in non-small cell lung cancer: state-of-the-art. , 2015, Translational lung cancer research.

[11]  Pierre Validire,et al.  Integrated molecular portrait of non-small cell lung cancers , 2013, BMC Medical Genomics.

[12]  C. Schumann,et al.  International tailored chemotherapy adjuvant trial: ITACA trial. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[14]  Igor Jurisica,et al.  Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[15]  S. Koscielny Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic , 2010, Science Translational Medicine.

[16]  Bengt Bergman,et al.  Long-term results of the international adjuvant lung cancer trial evaluating adjuvant Cisplatin-based chemotherapy in resected lung cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Richard Simon,et al.  Gene expression-based prognostic signatures in lung cancer: ready for clinical use? , 2010, Journal of the National Cancer Institute.

[18]  John J. Crowley,et al.  国际肺癌研究会分期项目——采用外科治疗的非小细胞肺癌的预后因素和病理TNM分期 , 2010, Zhongguo fei ai za zhi = Chinese journal of lung cancer.

[19]  L. Tanoue,et al.  The new lung cancer staging system. , 2009, Chest.

[20]  Masahiro Tsuboi,et al.  The present status of postoperative adjuvant chemotherapy for completely resected non-small cell lung cancer. , 2007, Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia.

[21]  Thorsten Meinl,et al.  KNIME: The Konstanz Information Miner , 2007, GfKl.

[22]  J. Ioannidis Why Most Published Research Findings Are False , 2005, PLoS medicine.

[23]  The eMERGE Clinical Annotation Working Group,et al.  Expression profiling — best practices for data generation and interpretation in clinical trials , 2004 .

[24]  Guidel Ines,et al.  Expression profiling — best practices for data generation and interpretation in clinical trials , 2004, Nature Reviews Genetics.

[25]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .