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Your search keyword '"Ikuan Sam"' showing total 2 results
Start Over Author "Ikuan Sam" Topic cancer research
2 results on '"Ikuan Sam"'

1. Cell graph neural networks enable the precise prediction of patient survival in gastric cancer

Author

Yanan Wang, Yu Guang Wang, Changyuan Hu, Ming Li, Yanan Fan, Nina Otter, Ikuan Sam, Hongquan Gou, Yiqun Hu, Terry Kwok, John Zalcberg, Alex Boussioutas, Roger J. Daly, Guido Montúfar, Pietro Liò, Dakang Xu, Geoffrey I. Webb, Jiangning Song, Li, Ming [0000-0002-1218-2804], Daly, Roger J [0000-0002-5739-8027], Lio, Pietro [0000-0002-0540-5053], Xu, Dakang [0000-0003-2415-4920], Webb, Geoffrey I [0000-0001-9963-5169], Song, Jiangning [0000-0001-8031-9086], Apollo - University of Cambridge Repository, and Liò, Pietro [0000-0002-0540-5053]

Subjects
Cancer Research, Rare Diseases, Oncology, 692/4028/67/1504/1829, 631/67/2321, article, 32 Biomedical and Clinical Sciences, 3 Good Health and Well Being, 631/114, 3211 Oncology and Carcinogenesis, 692/4028/67/327, Cancer
Abstract

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell−GraphSignatureorCGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan–Meier survival analysis indicates that the “digital grade” cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.

Published
2022

2. Plasma CD27, a Surrogate of the Intratumoral CD27-CD70 Interaction, Correlates with Immunotherapy Resistance in Renal Cell Carcinoma

Author

Nadine Benhamouda, Ikuan Sam, Nicolas Epaillard, Alain Gey, Letuan Phan, Hang Phuong Pham, Nadège Gruel, Antonin Saldmann, Joséphine Pineau, Milena Hasan, Valentin Quiniou, Camille Nevoret, Virginie Verkarre, Valentina Libri, Sebastien Mella, Clémence Granier, Chloe Broudin, Patrice Ravel, Eléonore De Guillebon, Laetitia Mauge, Dominique Helley, Bernd Jabla, Nathalie Chaput, Laurence Albiges, Sandrine Katsahian, Julien Adam, Arnaud Mejean, Olivier Adotevi, Yann A. Vano, Stéphane Oudard, Eric Tartour, Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Parean biotechnologies, Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Cytometrie et Biomarqueurs – Cytometry and Biomarkers (UTechS CB), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM), CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Institut Curie [Paris], Institut Gustave Roussy (IGR), Centre d'Investigation Clinique de Besançon (Inserm CIC 1431), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS BFC)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), This work was funded by the Fondation ARC pour la Recherche sur le Cancer (Grant number SIGN'IT20181007747 and PGA12019110000946_1581 to E. Tartour), the INCA (Institut National du Cancer) (Grant number 2016–1-PL BIO-05–1 to E. Tartour), the Agence Nationale de la Recherche (Labex Immuno-Oncology to E. Tartour), the Institut National du Cancer (Grant SIRIC CARPEM to E. Tartour, S. Oudard), FONCER (to E. Tartour and S. Oudard), IDEX Université de Paris (Grant number AMI-CD27CD70BLOC to E. Tartour), and Inserm Transfert (Grant number: MAT-PI-20278–1-02 to E. Tartour), We thank the staff of the tumor banks of HEGP (B. Vedie and D. Geromin) for providing the sample materials and the Histology platform of PARCC (C. Lesaffre). We thank Franck Pages and Florence Marliot for providing access to the Halo software platform., and ANR-10-LABX-0015,ImmunoOnco,Immuno-Oncology(2010)

Subjects
Cancer Research, Oncology, [SDV]Life Sciences [q-bio], Tumor Microenvironment, Humans, Immunotherapy, Carcinoma, Renal Cell, Kidney Neoplasms, CD27 Ligand, Tumor Necrosis Factor Receptor Superfamily, Member 7
Abstract

Purpose:CD70 is a costimulatory molecule known to activate CD27-expressing T cells. CD27–CD70 interaction leads to the release of soluble CD27 (sCD27). Clear-cell renal cell carcinoma (ccRCC) expresses the highest levels of CD70 among all solid tumors; however, the clinical consequences of CD70 expression remain unclear.Experimental Design:Tumor tissue from 25 patients with ccRCC was assessed for the expression of CD27 and CD70 in situ using multiplex immunofluorescence. CD27+ T-cell phenotypes in tumors were analyzed by flow cytometry and their gene expression profile were analyzed by single-cell RNA sequencing then confirmed with public data. Baseline sCD27 was measured in 81 patients with renal cell carcinoma (RCC) treated with immunotherapy (35 for training cohort and 46 for validation cohort).Results:In the tumor microenvironment, CD27+ T cells interacted with CD70-expressing tumor cells. Compared with CD27− T cells, CD27+ T cells exhibited an apoptotic and dysfunctional signature. In patients with RCC, the intratumoral CD27–CD70 interaction was significantly correlated with the plasma sCD27 concentration. High sCD27 levels predicted poor overall survival in patients with RCC treated with anti–programmed cell death protein 1 in both the training and validation cohorts but not in patients treated with antiangiogenic therapy.Conclusions:In conclusion, we demonstrated that sCD27, a surrogate marker of T-cell dysfunction, is a predictive biomarker of resistance to immunotherapy in RCC. Given the frequent expression of CD70 and CD27 in solid tumors, our findings may be extended to other tumors.

Published
2022

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