Proposal for a Combined Histomolecular Algorithm to Distinguish Multiple Primary Adenocarcinomas from Intrapulmonary Metastasis in Patients with Multiple Lung Tumors

Audrey Mansuet-Lupo; Marc Barritault; Marco Alifano; Aurélie Janet-Vendroux; Makmoud Zarmaev; Jérôme Biton; Yoan Velut; Christine Le Hay; Isabelle Cremer; Jean-François Régnard; Ludovic Fournel; Bastien Rance; Marie Wislez; Pierre Laurent-Puig; Ronald Herbst; Diane Damotte; Hélène Blons

doi:10.1016/j.jtho.2019.01.017

Proposal for a Combined Histomolecular Algorithm to Distinguish Multiple Primary Adenocarcinomas from Intrapulmonary Metastasis in Patients with Multiple Lung Tumors

J Thorac Oncol. 2019 May;14(5):844-856. doi: 10.1016/j.jtho.2019.01.017. Epub 2019 Feb 2.

Authors

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

Affiliations

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: [email protected].

PMID: 30721797
DOI: 10.1016/j.jtho.2019.01.017

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.

Keywords: Histological and molecular classification; Intrapulmonary metastasis; Multiple lung cancer; NGS.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adenocarcinoma / complications*
Adenocarcinoma / pathology
Adult
Aged
Aged, 80 and over
Algorithms
Female
High-Throughput Nucleotide Sequencing / methods*
Humans
Lung Neoplasms / pathology
Lung Neoplasms / secondary*
Male
Middle Aged
Neoplasm Metastasis
Neoplasms, Multiple Primary / complications*