Audrey Mansuet-Lupo1, Marc Barritault2, Marco Alifano3, Aurélie Janet-Vendroux4, Makmoud Zarmaev5, Jérôme Biton5, Yoan Velut5, Christine Le Hay6, Isabelle Cremer5, Jean-François Régnard3, Ludovic Fournel3, Bastien Rance7, Marie Wislez8, Pierre Laurent-Puig2, Ronald Herbst9, Diane Damotte10, Hélène Blons2. 1. Department of Pathology, Hôpitaux Universitaire Paris Centre, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; Institut National de la Santé et de la Recherche Médicale 1138, Team Cancer, Immune Control, and Escape, Centre de Recherche des Cordeliers, Université; Paris Descartes-Paris 5, Paris, France. 2. Department of Biochemistry, Unit of Pharmacogenetic and Molecular Oncology, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; INSERM UMR-S1147, Paris Sorbonne Cite University, Paris, France. 3. Department of Thoracic Surgery, Hôpitaux Universitaire Paris Centre, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. 4. Institut National de la Santé et de la Recherche Médicale 1138, Team Cancer, Immune Control, and Escape, Centre de Recherche des Cordeliers, Université; Paris Descartes-Paris 5, Paris, France; Department of Thoracic Surgery, Hôpitaux Universitaire Paris Centre, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. 5. Institut National de la Santé et de la Recherche Médicale 1138, Team Cancer, Immune Control, and Escape, Centre de Recherche des Cordeliers, Université; Paris Descartes-Paris 5, Paris, France. 6. INSERM UMR-S1147, Paris Sorbonne Cite University, Paris, France. 7. Department of Medical Informatics, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. 8. Institut National de la Santé et de la Recherche Médicale 1138, Team Cancer, Immune Control, and Escape, Centre de Recherche des Cordeliers, Université; Paris Descartes-Paris 5, Paris, France; Department of Pneumology, Hôpitaux Universitaire Paris Centre, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. 9. Department of Oncology Research, MedImmune, Gaithersburg, Maryland. 10. Department of Pathology, Hôpitaux Universitaire Paris Centre, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; Institut National de la Santé et de la Recherche Médicale 1138, Team Cancer, Immune Control, and Escape, Centre de Recherche des Cordeliers, Université; Paris Descartes-Paris 5, Paris, France. Electronic address: diane.damotte@aphp.fr.
Abstract
INTRODUCTION: Multiple nodules in the lung are being diagnosed with an increasing frequency thanks to high-quality computed tomography imaging. In patients with lung cancer, this situation represents up to 10% of patients who have an operation. For clinical management, it is important to classify the disease as intrapulmonary metastasis or multiple primary lung carcinoma to define TNM classification and optimize therapeutic options. In the present study, we evaluated the respective and combined input of histological and molecular classification to propose a classification algorithm for multiple nodules. METHODS: We studied consecutive patients undergoing an operation with curative intent for lung adenocarcinoma (N = 120) and harboring two tumors (N = 240). Histological diagnosis according to the WHO 2015 classification and molecular profiling using next-generation sequencing targeting 22 hotspot genes allowed classification of samples as multiple primary lung adenocarcinomas or as intrapulmonary metastasis. RESULTS: Next-generation sequencing identified molecular mutations in 91% of tumor pairs (109 of 120). Genomic and histological classification showed a fair agreement when the κ test was used (κ = 0.43). Discordant cases (30 of 109 [27%]) were reclassified by using a combined histomolecular algorithm. EGFR mutations (p = 0.03) and node involvement (p = 0.03) were significantly associated with intrapulmonary metastasis, whereas KRAS mutations (p = 0.00005) were significantly associated with multiple primary lung adenocarcinomas. EGFR mutations (p = 0.02) and node involvement (p = 0.004) were the only independent prognostic factors. CONCLUSION: We showed that combined histomolecular algorithm represents a relevant tool to classify multifocal lung cancers, which could guide adjuvant treatment decisions. Survival analysis underlined the good prognosis of EGFR-mutated adenocarcinoma in patients with intrapulmonary metastasis.
INTRODUCTION: Multiple nodules in the lung are being diagnosed with an increasing frequency thanks to high-quality computed tomography imaging. In patients with lung cancer, this situation represents up to 10% of patients who have an operation. For clinical management, it is important to classify the disease as intrapulmonary metastasis or multiple primary lung carcinoma to define TNM classification and optimize therapeutic options. In the present study, we evaluated the respective and combined input of histological and molecular classification to propose a classification algorithm for multiple nodules. METHODS: We studied consecutive patients undergoing an operation with curative intent for lung adenocarcinoma (N = 120) and harboring two tumors (N = 240). Histological diagnosis according to the WHO 2015 classification and molecular profiling using next-generation sequencing targeting 22 hotspot genes allowed classification of samples as multiple primary lung adenocarcinomas or as intrapulmonary metastasis. RESULTS: Next-generation sequencing identified molecular mutations in 91% of tumor pairs (109 of 120). Genomic and histological classification showed a fair agreement when the κ test was used (κ = 0.43). Discordant cases (30 of 109 [27%]) were reclassified by using a combined histomolecular algorithm. EGFR mutations (p = 0.03) and node involvement (p = 0.03) were significantly associated with intrapulmonary metastasis, whereas KRAS mutations (p = 0.00005) were significantly associated with multiple primary lung adenocarcinomas. EGFR mutations (p = 0.02) and node involvement (p = 0.004) were the only independent prognostic factors. CONCLUSION: We showed that combined histomolecular algorithm represents a relevant tool to classify multifocal lung cancers, which could guide adjuvant treatment decisions. Survival analysis underlined the good prognosis of EGFR-mutated adenocarcinoma in patients with intrapulmonary metastasis.
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