S Le Guellec1, T Lesluyes2, E Sarot3, C Valle4, T Filleron5, P Rochaix6, T Valentin7, G Pérot8, J-M Coindre9, F Chibon6. 1. Department of Pathology, Institut Claudius Regaud, IUCT-Oncopole, Toulouse, France; INSERM U1037, Cancer Research Center of Toulouse (CRCT), Toulouse, France. Electronic address: LeGuellec.sophie@iuct-oncopole.fr. 2. INSERM U1037, Cancer Research Center of Toulouse (CRCT), Toulouse, France; INSERM U1218, Institut Bergonié, Bordeaux, France; University of Bordeaux, Bordeaux, France; Institut Claudius Regaud, IUCT-Oncopole, Toulouse, France. 3. Institut Claudius Regaud, IUCT-Oncopole, Toulouse, France; Plateau Génomique et Transcriptomique, INSERM U1037, Cancer Research Center of Toulouse (CRCT), Pôle technologique, Toulouse; Departments of, France. 4. Plateau Génomique et Transcriptomique, INSERM U1037, Cancer Research Center of Toulouse (CRCT), Pôle technologique, Toulouse; Departments of, France. 5. Department of Biostatistics. 6. Department of Pathology, Institut Claudius Regaud, IUCT-Oncopole, Toulouse, France; INSERM U1037, Cancer Research Center of Toulouse (CRCT), Toulouse, France. 7. INSERM U1037, Cancer Research Center of Toulouse (CRCT), Toulouse, France; Oncology, Institut Claudius Regaud, IUCT-Oncopole, Toulouse, France. 8. INSERM U1218, Institut Bergonié, Bordeaux, France; Department of Biopathology, Institut Bergonié, Bordeaux, France. 9. University of Bordeaux, Bordeaux, France; Department of Biopathology, Institut Bergonié, Bordeaux, France.
Abstract
Background: Prediction of metastatic outcome in sarcomas is challenging for clinical management since they are aggressive and carry a high metastatic risk. A 67-gene expression signature, the Complexity INdex in SARComas (CINSARC), has been identified as a better prognostic factor than the reference pathological grade. Since it cannot be applied easily in standard laboratory practice, we assessed its prognostic value using nanoString on formalin-fixed, paraffin-embedded (FFPE) blocks to evaluate its potential in clinical routine practice and guided therapeutic management. Methods: A code set consisting of 67 probes derived from the 67 genes of the CINSARC signature was built and named NanoCind®. To compare the performance of RNA-seq and nanoString (NanoCind®), we used expressions of various sarcomas (n = 124, frozen samples) using both techniques and compared predictive values based on CINSARC risk groups and clinical annotations. We also used nanoString on FFPE blocks (n = 67) and matching frozen and FFPE samples (n = 45) to compare their level of agreement. Metastasis-free survival and agreement values in classification groups were evaluated. Results: CINSARC strongly predicted metastatic outcome using nanoString on frozen samples (HR = 2.9, 95% CI: 1.23-6.82) with similar risk-group classifications (86%). While more than 50% of FFPE blocks were not analyzable by RNA-seq owing to poor RNA quality, all samples were analyzable with nanoString. When similar (risk-group) classifications were measured with frozen tumors (RNA-seq) compared with FFPE blocks (84% agreement), the CINSARC signature was still a predictive factor of metastatic outcome with nanoString on FFPE samples (HR = 4.43, 95% CI: 1.25-15.72). Conclusion: CINSARC is a material-independent prognostic signature for metastatic outcome in sarcomas and outperforms histological grade. Unlike RNA-seq, nanoString is not influenced by the poor quality of RNA extracted from FFPE blocks. The CINSARC signature can potentially be used in combination with nanoString (NanoCind®) in routine clinical practice on FFPE blocks to predict metastatic outcome.
Background: Prediction of metastatic outcome in sarcomas is challenging for clinical management since they are aggressive and carry a high metastatic risk. A 67-gene expression signature, the Complexity INdex in SARComas (CINSARC), has been identified as a better prognostic factor than the reference pathological grade. Since it cannot be applied easily in standard laboratory practice, we assessed its prognostic value using nanoString on formalin-fixed, paraffin-embedded (FFPE) blocks to evaluate its potential in clinical routine practice and guided therapeutic management. Methods: A code set consisting of 67 probes derived from the 67 genes of the CINSARC signature was built and named NanoCind®. To compare the performance of RNA-seq and nanoString (NanoCind®), we used expressions of various sarcomas (n = 124, frozen samples) using both techniques and compared predictive values based on CINSARC risk groups and clinical annotations. We also used nanoString on FFPE blocks (n = 67) and matching frozen and FFPE samples (n = 45) to compare their level of agreement. Metastasis-free survival and agreement values in classification groups were evaluated. Results: CINSARC strongly predicted metastatic outcome using nanoString on frozen samples (HR = 2.9, 95% CI: 1.23-6.82) with similar risk-group classifications (86%). While more than 50% of FFPE blocks were not analyzable by RNA-seq owing to poor RNA quality, all samples were analyzable with nanoString. When similar (risk-group) classifications were measured with frozen tumors (RNA-seq) compared with FFPE blocks (84% agreement), the CINSARC signature was still a predictive factor of metastatic outcome with nanoString on FFPE samples (HR = 4.43, 95% CI: 1.25-15.72). Conclusion: CINSARC is a material-independent prognostic signature for metastatic outcome in sarcomas and outperforms histological grade. Unlike RNA-seq, nanoString is not influenced by the poor quality of RNA extracted from FFPE blocks. The CINSARC signature can potentially be used in combination with nanoString (NanoCind®) in routine clinical practice on FFPE blocks to predict metastatic outcome.
Authors: J Attal; B Cabarrou; T Valentin; J P Nesseler; E Stoeckle; A Ducassou; T Filleron; S Le Guellec; B Boulet; G Vogin; G Ferron; E Cohen-Jonathan Moyal; M Delannes; C Chevreau Journal: Strahlenther Onkol Date: 2021-10-21 Impact factor: 3.621
Authors: Jens Jakob; Tom Lesluyes; Anna Simeonova-Chergou; Frederik Wenz; Peter Hohenberger; Frederic Chibon; Sophie Le Guellec Journal: Strahlenther Onkol Date: 2019-11-15 Impact factor: 3.621