| Literature DB >> 23034130 |
Jérôme Galon1, Franck Pagès, Francesco M Marincola, Helen K Angell, Magdalena Thurin, Alessandro Lugli, Inti Zlobec, Anne Berger, Carlo Bifulco, Gerardo Botti, Fabiana Tatangelo, Cedrik M Britten, Sebastian Kreiter, Lotfi Chouchane, Paolo Delrio, Hartmann Arndt, Martin Asslaber, Michele Maio, Giuseppe V Masucci, Martin Mihm, Fernando Vidal-Vanaclocha, James P Allison, Sacha Gnjatic, Leif Hakansson, Christoph Huber, Harpreet Singh-Jasuja, Christian Ottensmeier, Heinz Zwierzina, Luigi Laghi, Fabio Grizzi, Pamela S Ohashi, Patricia A Shaw, Blaise A Clarke, Bradly G Wouters, Yutaka Kawakami, Shoichi Hazama, Kiyotaka Okuno, Ena Wang, Jill O'Donnell-Tormey, Christine Lagorce, Graham Pawelec, Michael I Nishimura, Robert Hawkins, Réjean Lapointe, Andreas Lundqvist, Samir N Khleif, Shuji Ogino, Peter Gibbs, Paul Waring, Noriyuki Sato, Toshihiko Torigoe, Kyogo Itoh, Prabhu S Patel, Shilin N Shukla, Richard Palmqvist, Iris D Nagtegaal, Yili Wang, Corrado D'Arrigo, Scott Kopetz, Frank A Sinicrope, Giorgio Trinchieri, Thomas F Gajewski, Paolo A Ascierto, Bernard A Fox.
Abstract
Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).Entities:
Mesh:
Year: 2012 PMID: 23034130 PMCID: PMC3554496 DOI: 10.1186/1479-5876-10-205
Source DB: PubMed Journal: J Transl Med ISSN: 1479-5876 Impact factor: 5.531
Figure 1Immunoscore definition and method.
Current Immunoscore procedure and reagents
| Tumor selection | Block which is the most infiltrated by the immune cells and containing the core of the tumor (CT) and the invasive margin (IM) |
| Sample preparation | 2 paraffin sections of 4-microns of the tumor block deposited in deionized water on Superfrost-plus slides |
| Immuno-histochemistry (IHC) | 2 single stainings using IVD certified antibodies |
| Antigen retrieval | CC1 tris-based buffer pH8 |
| Primary antibody | CD3 (2GV6, Ventana) and CD8 (C8/144, Dako) |
| Primary antibody diluant | K 004 (Clinisciences) for CD8 |
| Secondary reagents | Ultraview TM DAB (Ventana) |
| Counterstaining | Hematoxillin II (Ventana) |
| Autostrainer | Benchmark XT (Ventana) |
| Scanner | NanoZoomer 2.0-HT (Hammamatsu) |
| Digital pathology | Architect XD software (Definiens) |
| Immunoscore quantification | Immunoscore Plug-in (INSERM / AP-HP) |
Characteristics of a good marker and of the Immunoscore
| Routine | YES | Technic to be performed by pathologist using bright field and precise cell evaluation |
| Feasible | YES | Established pathology technics, using 2 regular whole slide FFPE section |
| Inexpensive | YES | Automatized immunohistochemistry |
| Rapid | YES | 2 simple staining less costly than complicated molecular techniccs |
| Robust | YES | Autostainers, scanner, and digital pathology reduce the time to perform an Immunoscore |
| Reproducible | YES | Two strong membrane staining, with no background, allowing the numeration of individual cells |
| Quantitative | YES | Inter-observers variability is removed by the use of digital pathology, taking into account cell location and counts |
| Standardized | YES | Standardized operating procedure should be performed to insure reproducibility and worldwide comparisons |
| Pathology-base | YES | Necessity of pathologist expertise to validate cell type, cell location, and cell counts performed by digital pathology |
| Powerful | YES | The immunoscore has a prognostic value highly significant even in Cox multivariate including TNM classification13 |
Figure 2Worldwide expert centers participating in the Immunoscore task force.