肺神经内分泌肿瘤和超类癌的深度分子分析
Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids.
作者
作者单位
- Computational Cancer Genomics Team, Genomic Epidemiology Branch, International Agency for Research On Cancer (IARC-WHO), 25 Avenue Tony Garnier, Lyon CEDEX 07, 69372, France.
- European Molecular Biology Laboratory, Barcelona, Spain.
- Instituto de Ciencias de La Ingeniería, Universidad de O'Higgins, Rancagua, Chile.
- Department of Computational Biology, Cornell University, New York, USA.
- Department of Pathology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands.
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Cordoba, Spain.
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Department of Biology, Stanford University, Stanford, CA, USA.
- Interdisciplinary Department of Medicine, University of Bari "Aldo Moro", Bari, Italy.
- Department of Internal Medicine, Division of Oncology, Medical University of Graz, Graz, Austria.
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Cancer Genomic Platform, Centre de Recherche en Cancérologie de Lyon (CRCL) INSERM U1052-CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon1, Centre Léon Bérard, Lyon, France.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Epigenomics and Mechanisms Branch, International Agency for Research On Cancer (IARC-WHO), Lyon, France.
- Department of Pathology and Laboratory Medicine, First Pathology Division, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy.
- CNRS 5286, Lyon 1 University & Department of Biopathology, Pathology Research Platform, Cancer Research Center of Lyon, INSERM 1052, CNR MESOPATH NETMESO, Centre Léon Bérard, Lyon, France.
- Department of Pathology, Hôpital Marie-Lannelongue, Groupe Hospitalier Paris Saint Joseph, Le Plessis Robinson, France.
- Centre National de Recherche en Génomique Humaine (CNRGH), CEA-Institut de Biologie François Jacob, Université Paris-Saclay, Evry, France.
- Department of Pulmonary Medicine, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands.
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Rome, Italy.
- IHU RespirERA, Laboratory of Clinical and Experimental Pathology, FHU OncoAge, Côte d'Azur University, Nice, France.
- Center of Biological Resources BB-0033-00035, University Hospital of Nancy, Vandœuvre‑lès‑Nancy, France.
- Department of Pathology, Oslo University Hospital, Oslo, Norway.
- Laboratory of Oncology, Fondazione IRCCS Casa Sollievo Della Sofferenza, San Giovanni Rotondo, Italy.
- Hospices Civils de Lyon Biobank (CRB HCL), Tissu-Tumorothèque Est, Lyon, France.
- Pathology Department, Caen University Hospital, Normandy University, Caen, France.
- Pathology Institute for Diagnostics and Research, Medical University Graz, Graz, Austria.
- Department of Experimental Oncology, Fondazione IRCCS - Istituto Nazionale Dei Tumori, Milan, Italy.
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands.
- Plateforme de Gestion Des Echantillons Biologique, Centre Léon Bérard, Cancer Research Center of Lyon, Université de Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Lyon, France.
- Gastroenterology and Technologies for Health, INSERM UMR 1052 CNRS UMR 5286, Cancer Research Center of Lyon, University of Lyon, Lyon, France.
- University of Melbourne, Melbourne, Australia.
- Department of Pathology, Groupe Hospitalier Est, Hospices Civils de Lyon, Bron, France.
- Department of Mathematics and Informatics, Ecole Centrale de Lyon, Lyon, France.
- CISTAR Team, Cancer Research Center of Lyon, INSERM U1052, CNRS UMR5286, Université de Lyon, 8 Université Lyon 1, Centre Léon Bérard, Lyon, France.
- Department of Oncology, University of Turin at San Luigi Hospital, Orbassano, Turin, Italy.
- Institut Curie, Paris, France.
- Department of Biopathologie, Nancy University Hospital, Nancy, France.
- Department of Pathology, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid, Madrid, Spain.
- Department of Pathology, Cochin Hospital, APHP Paris Centre, Paris City University, Paris, France.
- Department of Pathology, Hospital Graz II, Graz, Austria.
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
- Department of Oncology, University of Turin, Turin, Italy.
- Grenoble Alpes University, Grenoble, France.
- Computational Cancer Genomics Team, Genomic Epidemiology Branch, International Agency for Research On Cancer (IARC-WHO), 25 Avenue Tony Garnier, Lyon CEDEX 07, 69372, France. alcalan@iarc.who.int.
- Computational Cancer Genomics Team, Genomic Epidemiology Branch, International Agency for Research On Cancer (IARC-WHO), 25 Avenue Tony Garnier, Lyon CEDEX 07, 69372, France. follm@iarc.who.int.
- Computational Cancer Genomics Team, Genomic Epidemiology Branch, International Agency for Research On Cancer (IARC-WHO), 25 Avenue Tony Garnier, Lyon CEDEX 07, 69372, France. fernandezcuestal@iarc.who.int.
摘要
中文
肺神经内分泌肿瘤（NETs，也称类癌）的发病率在全球范围内迅速上升，但病因未知，除手术外治疗选择有限。目前的WHO分类基于核分裂计数和有无坏死，将肺NETs分为1级典型类癌和2级非典型类癌。然而，这种二分法分类并未涵盖最近描述的分子实体，也不足以满足临床管理需求。在此，我们对超过300例肺NETs进行了整合多组学分析，包括全基因组测序、转录组分析和DNA甲基化芯片，随后进行原型分析，以识别和描述分子组。我们进一步使用空间RNA测序和蛋白质组学，以及全切片图像的深度学习分析来研究分子组。多组学数据的整合明确证明存在四个显著不同的分子组，这些组在患者特征、基因组和转录组图谱、微环境及形态学上各不相同。其中，我们识别出一个新的分子组，富含高度侵袭性的超类癌，其表现出与肿瘤-巨噬细胞串扰相关的免疫富集微环境。我们揭示了超类癌中的未分化细胞群体，并展示了超类癌与最近发现的非典型小细胞肺癌肿瘤之间的转录组相似性，进一步证明了它们与高级别肺神经内分泌癌的分子联系。多区域基因组分析确定了不同的进化轨迹，表明分子组在肿瘤发生早期由基因组事件决定，并且组间转换虽然不常见，但对于超类癌是可能的。深度学习模型仅基于形态学就能准确识别这些组，优于当前的组织学标准。结合免疫组织化学标记物组的验证，我们证明这些分子组可以基于形态学特征被准确识别，从而促进其在临床环境中的未来实施。我们提出的形态-分子分类突出了潜在的组特异性治疗机会，在DLL3、EGFR、FGFR和TERT抑制剂靶点的表达上存在差异。总体而言，我们的发现统一了先前提出的分子分类，并通过揭示具有潜在预后和治疗管理意义的新肿瘤表型，完善了肺癌图谱。
English
Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limited therapeutic options beyond surgery. The current WHO classification, based on mitotic count and presence or absence of necrosis, divides lung NETs into grade-1 typical, and grade-2 atypical tumours. This dichotomous classification however does not account for recently described molecular entities nor is it sufficient for clinical management. Here we conducted integrative multi-omic analyses on over 300 lung NETs including whole-genome sequencing, transcriptome profiling, and DNA methylation arrays, followed by archetype analysis, to identify and characterise molecular groups. We further investigated molecular groups using spatial RNA sequencing and proteomics, and deep learning analysis of whole slide images. The integration of multi-omic data provided definitive proof of the existence of four strikingly different molecular groups that vary in patient characteristics, genomic and transcriptomic profiles, microenvironment, and morphology. Among these, we identified a new molecular group, enriched for highly aggressive supra-carcinoids that displayed an immune-rich microenvironment linked to tumour-macrophage crosstalk. We uncovered an undifferentiated cell population within supra-carcinoids and show the transcriptomic similarities between supra-carcinoids and the recently identified atypical small cell lung cancer tumours, further demonstrating their molecular link to high-grade lung neuroendocrine carcinomas. Multi-regional genomic analyses identified distinct evolutionary trajectories, suggesting that molecular groups are determined early in tumourigenesis by genomic events, and that transitions between groups, though infrequent, are possible for supra-carcinoids. Deep learning models accurately identified these groups based on morphology alone, outperforming current histological criteria. Together with the validation of a panel of immunohistochemistry markers, we demonstrated that these molecular groups can be accurately identified based on morphological features, facilitating their future implementation in the clinical setting. Our proposed morpho-molecular classification highlights potential group-specific therapeutic opportunities, with differences in expression to DLL3, EGFR, FGFR and TERT inhibitor targets. Overall, our findings unify previously proposed molecular classifications and refine the lung cancer map by revealing novel tumour phenotypes with potential implications for prognosis and therapeutic management.
分类与指标
- 研究类型
- 基础研究
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 42.2
- 新锐分区
- 1区