1. Identification of Tissue of Origin and Guided Therapeutic Applications in Cancers of Unknown Primary Using Deep Learning and RNA Sequencing (TransCUPtomics)
- Author
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Joshua J. Waterfall, Anne Vincent-Salomon, François-Clément Bidard, Alain Livartowski, Delphine Guillemot, Odette Mariani, Camille Benoist, Sylvain Baulande, Gaëlle Pierron, Christophe Le Tourneau, Sarah Watson, Julien Vibert, Olivier Delattre, Nadège Gruel, Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre d'Investigation Clinique en Biotherapie des cancers (CIC 1428 , CBT 507 ), Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Versailles Saint-Quentin-en-Yvelines - UFR Sciences de la santé Simone Veil (UVSQ Santé), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
- Subjects
Adult ,Male ,Oncology ,medicine.medical_specialty ,[SDV]Life Sciences [q-bio] ,Pathology and Forensic Medicine ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Internal medicine ,Biomarkers, Tumor ,medicine ,Humans ,Prospective Studies ,RNA-Seq ,Aged ,Retrospective Studies ,030304 developmental biology ,Interpretability ,Aged, 80 and over ,0303 health sciences ,business.industry ,Deep learning ,Cancer ,Retrospective cohort study ,Middle Aged ,medicine.disease ,Primary tumor ,Autoencoder ,Data Accuracy ,3. Good health ,Identification (information) ,030220 oncology & carcinogenesis ,Unknown primary ,Neoplasms, Unknown Primary ,Molecular Medicine ,Female ,Artificial intelligence ,Gene Fusion ,Transcriptome ,business - Abstract
International audience; Cancers of unknown primary (CUP) are metastatic cancers for which the primary tumor is not found despite thorough diagnostic investigations. Multiple molecular assays have been proposed to identify the tissue of origin (TOO) and inform clinical care; however, none has been able to combine accuracy, interpretability, and easy access for routine use. We developed a classifier tool based on the training of a variational autoencoder to predict tissue of origin based on RNA-sequencing data. We used as training data 20,918 samples corresponding to 94 different categories, including 39 cancer types and 55 normal tissues. The TransCUPtomics classifier was applied to a retrospective cohort of 37 CUP patients and 11 prospective patients. TransCUPtomics exhibited an overall accuracy of 96% on reference data for TOO prediction. The TOO could be identified in 38 (79%) of 48 CUP patients. Eight of 11 prospective CUP patients (73%) could receive first-line therapy guided by TransCUPtomics prediction, with responses observed in most patients. The variational autoencoder added further utility by enabling prediction interpretability, and diagnostic predictions could be matched to detection of gene fusions and expressed variants. TransCUPtomics confidently predicted TOO for CUP and enabled tailored treatments leading to significant clinical responses. The interpretability of our approach is a powerful addition to improve the management of CUP patients. Copyright
- Published
- 2021