This study was funded by European Union Seventh Framework Program (FP7/2007-2013) under grant agreement (grant number: 259735), Horizon 2020 (grant number: 633983), Institut National du Cancer and Direction Generale de l'Offre de Soins (DGOS), Programme de Recherche Translationnelle en cancerologie (PRT-K 2014, COMETE-TACTIC, INCa_DGOS_8663), the Plan Cancer: Appel a projets Epigenetique et Cancer 2013 (EPIG201303 METABEPIC), Agence Nationale de la Recherche (ANR-2011-JCJC-00701 MODEOMAPP), Instituto de Salud Carlos III (ISCIII), Accion Estrategica en Salud, cofounded by FEDER (PI14/00240 and PI17/01796) and the Paradifference Foundation. Bruna Calsina was supported by Rafael del Pino Foundation (Becas de ExcelenciaRafael del Pino 2017) and currently by ISCIII project PI17/01796. We thank all members of the Genetics Department, Biological Resources Center and Tumor Bank Platform, Hopital europeeen Georges Pompidou (BB-0033-00063) for technical support. ; Sí
Phaeochromocytomas and paragangliomas (PPGLs) are neural-crest-derived tumours of the sympathetic or parasympathetic nervous system that are often inherited and are genetically heterogeneous. Genetic testing is recommended for patients with these tumours and for family members of patients with hereditary forms of PPGLs. Due to the large number of susceptibility genes implicated in the diagnosis of inherited PPGLs, next-generation sequencing (NGS) technology is ideally suited for carrying out genetic screening of these individuals. This Consensus Statement, formulated by a study group comprised of experts in the field, proposes specific recommendations for the use of diagnostic NGS in hereditary PPGLs. In brief, the study group recommends target gene panels for screening of germ line DNA, technical adaptations to address different modes of disease transmission, orthogonal validation of NGS findings, standardized classification of variant pathogenicity and uniform reporting of the findings. The use of supplementary assays, to aid in the interpretation of the results, and sequencing of tumour DNA, for identification of somatic mutations, is encouraged. In addition, the study group launches an initiative to develop a gene-centric curated database of PPGL variants, with annual re-evaluation of variants of unknown significance by an expert group for purposes of reclassification and clinical guidance. ; Funding Agencies|Cancer Prevention and Research Institute of Texas (CPRIT) [RP101202, RP57154]; Department of Defense [CDMRP W81XWH-12-1-0508]; Voelcker Fund; National Institutes of Health (NIH)s National Center for Research Resources; National Center for Advancing Translational Sciences [8UL1TR000149]; INSERM; French National Cancer Institute (INCA); Direction Generale de lOffre de Soins (DGOS); INCA [INCA-DGOS_8663]; European Union [633983]; Brazilian National Council for Scientific and Technological Development (CNPq); Cancer Research for PErsonalized Medicine (CARPEM); ERC Advanced Researcher Award
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types.