Gastric cancer (GC) is the world's third leading cause of cancer mortality. In spite of significant therapeutic improvements, the clinical outcome for patients with advanced GC is poor; thus, the identification and validation of novel targets is extremely important from a clinical point of view. We generated a wide, multi-level platform of GC models, comprising 100 patient-derived xenografts (PDXs), primary cell lines and organoids. Samples were classified according to their histology, microsatellite stability (MS), Epstein-Barr virus status, and molecular profile. This PDX platform is the widest in an academic institution and it includes all the GC histologic and molecular types identified by TCGA. PDX histopathological features were consistent with those of patients' primary tumors and were maintained throughout passages in mice. Factors modulating grafting rate were histology, TNM stage, copy number gain of tyrosine kinases/KRAS genes and MS status. PDX and PDX-derived cells/organoids demonstrated potential usefulness to study targeted therapy response. Finally, PDX transcriptomic analysis identified a cancer cell intrinsic MSI signature, which was efficiently exported to gastric cancer, allowing the identification -among MSS patients- of a subset of MSI-like tumors with common molecular aspects and significant better prognosis. In conclusion, we generated a wide gastric cancer PDX platform, whose exploitation will help identify and validate novel 'druggable' targets and optimize therapeutic strategies. Moreover, transcriptomic analysis of GC PDXs allowed the identification of a cancer cell intrinsic MSI signature, recognizing a subset of MSS patients with MSI transcriptional traits, endowed with better prognosis.

A comprehensive PDX gastric cancer collection captures cancer cell intrinsic transcriptional MSI traits

Corso, Simona
First
;
Isella, Claudio;Bellomo, Sara E;Apicella, Maria;Durando, Stefania;Migliore, Cristina;Ughetto, Stefano;D'Errico, Laura;Menegon, Silvia;Moya-Rull, Daniel;Cargnelutti, Marilisa;Capeloa, Tania;Conticelli, Daniela;Giordano, Jessica;Venesio, Tiziana;Marchiò, Caterina;Degiuli, Maurizio;Reddavid, Rossella;De Simone, Michele;Medico, Enzo;Cassoni, Paola;Sapino, Anna;Giordano, Silvia
Last
2019-01-01

Abstract

Gastric cancer (GC) is the world's third leading cause of cancer mortality. In spite of significant therapeutic improvements, the clinical outcome for patients with advanced GC is poor; thus, the identification and validation of novel targets is extremely important from a clinical point of view. We generated a wide, multi-level platform of GC models, comprising 100 patient-derived xenografts (PDXs), primary cell lines and organoids. Samples were classified according to their histology, microsatellite stability (MS), Epstein-Barr virus status, and molecular profile. This PDX platform is the widest in an academic institution and it includes all the GC histologic and molecular types identified by TCGA. PDX histopathological features were consistent with those of patients' primary tumors and were maintained throughout passages in mice. Factors modulating grafting rate were histology, TNM stage, copy number gain of tyrosine kinases/KRAS genes and MS status. PDX and PDX-derived cells/organoids demonstrated potential usefulness to study targeted therapy response. Finally, PDX transcriptomic analysis identified a cancer cell intrinsic MSI signature, which was efficiently exported to gastric cancer, allowing the identification -among MSS patients- of a subset of MSI-like tumors with common molecular aspects and significant better prognosis. In conclusion, we generated a wide gastric cancer PDX platform, whose exploitation will help identify and validate novel 'druggable' targets and optimize therapeutic strategies. Moreover, transcriptomic analysis of GC PDXs allowed the identification of a cancer cell intrinsic MSI signature, recognizing a subset of MSS patients with MSI transcriptional traits, endowed with better prognosis.
2019
79
22
5884
5896
Corso, Simona; Isella, Claudio; Bellomo, Sara E; Apicella, Maria; Durando, Stefania; Migliore, Cristina; Ughetto, Stefano; D'Errico, Laura; Menegon, S...espandi
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Descrizione: Cancer Res. 2019 Nov 15;79(22):5884-5896
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1717860
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