Raphael Bueno1, William G Richards2, David H Harpole3, Karla V Ballman4, Ming-Sound Tsao5, Zhengming Chen4, Xiaofei Wang6, Guoan Chen7, Lucian R Chirieac8, M Herman Chui9, Wilbur A Franklin10, Thomas J Giordano11, Ramaswamy Govindan12, Mary-Beth Joshi3, Daniel T Merrick10, Christopher J Rivard13, Thomas Sporn3, Adrie van Bokhoven10, Hui Yu13, Frances A Shepherd14, Mark A Watson15, David G Beer7, Fred R Hirsch16. 1. Department of Surgery, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts. Electronic address: rbueno@bwh.harvard.edu. 2. Department of Surgery, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts. 3. Duke Cancer Institute, Duke University, Durham, North Carolina. 4. Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York. 5. Department of Laboratory Medicine and Pathobiology, University Health Network/Princess Margaret Cancer Centre and University of Toronto, Toronto, Canada. 6. Alliance Statistics and Data Center, Duke University, Durham, North Carolina. 7. Department of Surgery, University of Michigan, Ann Arbor, Michigan. 8. Department of Pathology, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts. 9. Department of Pathology, University Health Network/Princess Margaret Cancer Centre and University of Toronto, Toronto, Canada. 10. Department of Pathology, University of Colorado Denver, Aurora, Colorado. 11. Department of Pathology, University of Michigan, Ann Arbor, Michigan. 12. Division of Oncology, Washington University School of Medicine, St. Louis, Missouri. 13. Division of Medical Oncology, University of Colorado Denver, Aurora, Colorado. 14. Division of Medical Oncology, University Health Network/Princess Margaret Cancer Centre and University of Toronto, Toronto, Canada. 15. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri. 16. Division of Medical Oncology, University of Colorado Denver, Aurora, Colorado; Tisch Cancer Institute, Mount Sinai Health System, New York, New York.
Abstract
INTRODUCTION: Surgical resection is curative for some patients with early lung squamous cell carcinoma. Staging and clinical factors do not adequately predict recurrence risk. We sought to validate the discriminative performance of proposed prognostic gene expression signatures at a level of rigor sufficient to support clinical use. METHODS: The two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen and data sets. The first stage validation confirmed a signature's ability to stratify patient survival. The second-stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled in institutional (cohort I) or cooperative group (cohort II) biospecimen and data collection protocols. All cases underwent a central review of clinical, pathologic, and biospecimen parameters using objective criteria to determine final inclusion (cohort I: n = 249; cohort II: n = 234). Primary selection required that a signature significantly predict a 3-year survival after surgical resection in cohort I. Signatures meeting this criterion were further tested in cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature. RESULTS: Male sex, advanced age, and higher stage were associated with shorter survival in cohort I and established a baseline clinical model. Of the three signatures validated in cohort I, one signature was validated in cohort II and statistically significantly enhanced the prognosis relative to the baseline model (C-index difference 0.122; p < 0.05). CONCLUSIONS: These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
INTRODUCTION: Surgical resection is curative for some patients with early lung squamous cell carcinoma. Staging and clinical factors do not adequately predict recurrence risk. We sought to validate the discriminative performance of proposed prognostic gene expression signatures at a level of rigor sufficient to support clinical use. METHODS: The two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen and data sets. The first stage validation confirmed a signature's ability to stratify patient survival. The second-stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled in institutional (cohort I) or cooperative group (cohort II) biospecimen and data collection protocols. All cases underwent a central review of clinical, pathologic, and biospecimen parameters using objective criteria to determine final inclusion (cohort I: n = 249; cohort II: n = 234). Primary selection required that a signature significantly predict a 3-year survival after surgical resection in cohort I. Signatures meeting this criterion were further tested in cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature. RESULTS: Male sex, advanced age, and higher stage were associated with shorter survival in cohort I and established a baseline clinical model. Of the three signatures validated in cohort I, one signature was validated in cohort II and statistically significantly enhanced the prognosis relative to the baseline model (C-index difference 0.122; p < 0.05). CONCLUSIONS: These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
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