Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Supporting decision making and early prediction of survival for oncology drug development using a pharmacometrics-machine learning based model

International audienc

Exploring the complementarity of pancreatic ductal adenocarcinoma preclinical models

Purpose: Compare pancreatic ductal adenocarcinoma (PDAC), preclinical models, by their transcriptome and drug response landscapes to evaluate their complementarity. Experimental De-sign: Three paired PDAC preclinical models—patient‐derived xenografts (PDX), xenograft‐derived pancreatic organoids (XDPO) and xenograft‐derived primary cell cultures (XDPCC)—were derived from 20 patients and analyzed at the transcriptomic and chemosensitivity level. Transcriptomic characterization was performed using the basal‐like/classical subtyping and the PDAC molecular gradient (PAMG). Chemosensitivity for gemcitabine, irinotecan, 5‐fluorouracil and oxaliplatin was established and the associated biological pathways were determined using independent component analysis (ICA) on the transcriptome of each model. The selection criteria used to identify the different components was the chemosensitivity score (CSS) found for each drug in each model. Results: PDX was the most dispersed model whereas XDPO and XDPCC were mainly classical and basal-like, respectively. Chemosensitivity scoring determines that PDX and XDPO display a positive correlation for three out of four drugs tested, whereas PDX and XDPCC did not correlate. No match was observed for each tumor chemosensitivity in the different models. Finally, pathway analysis shows a significant association between PDX and XDPO for the chemosensitivity‐associated pathways and PDX and XDPCC for the chemoresistance‐associated pathways. Conclusions: Each PDAC preclinical model possesses a unique basal‐like/classical transcriptomic phenotype that strongly in-fluences their global chemosensitivity. Each preclinical model is imperfect but complementary, sug-gesting that a more representative approach of the clinical reality could be obtained by combining them. Translational Relevance: The identification of molecular signatures that underpin drug sensitivity to chemotherapy in PDAC remains clinically challenging. Importantly, the vast majority of studies using preclinical in vivo and in vitro models fail when transferred to patients in a clinical setting despite initially promising results. This study presents for the first time a comparison between three preclinical models directly derived from the same patients. We show that their applica-bility to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features.Fil: Hoare, Owen. Centre National de la Recherche Scientifique; FranciaFil: Fraunhoffer Navarro, Nicolas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Elkaoutari, Abdessamad. Centre National de la Recherche Scientifique; FranciaFil: Gayet, Odile. Centre National de la Recherche Scientifique; FranciaFil: Bigonnet, Martin. Centre National de la Recherche Scientifique; FranciaFil: Roques, Julie. Centre National de la Recherche Scientifique; FranciaFil: Nicolle, Rémy. No especifíca;Fil: McGuckin, Colin. Cell Therapy Research Institute; FranciaFil: Forraz, Nico. Cell Therapy Research Institute; FranciaFil: Sohier, Emilie. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Tonon, Laurie. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Wajda, Pauline. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Boyault, Sandrine. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Attignon, Valéry. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Tabone, Luciana Belen. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Barbier, Sandrine. No especifíca;Fil: Mignard, Caroline. No especifíca;Fil: Duchamp, Olivier. No especifíca;Fil: Iovanna, Juan. Centre National de la Recherche Scientifique; FranciaFil: Dusetti, Nelson J.. Centre National de la Recherche Scientifique; Franci

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Basal‐like and classical cells coexist in pancreatic cancer revealed by single‐cell analysis on biopsy‐derived pancreatic cancer organoids from the classical subtype

International audiencePancreatic ductal adenocarcinoma (PDAC) is composed of stromal, immune, and cancerous epithelial cells. Transcriptomic analysis of the epithelial compartment allows classification into different phenotypic subtypes as classical and basal-like. However, little is known about the intra-tumor heterogeneity particularly in the epithelial compartment. Growing evidences suggest that this phenotypic segregation is not so precise and different cancerous cell types may coexist in a single tumor. To test this hypothesis, we performed single-cell transcriptomic analyses using combinational barcoding exclusively on epithelial cells from six different classical PDAC patients obtained by Endoscopic Ultrasound (EUS) with Fine Needle Aspiration (FNA). To purify the epithelial compartment, PDAC were grown as biopsy-derived pancreatic cancer organoids. Single-cell transcriptomic analysis allowed the identification of four main cell clusters present in different proportions in all tumors. Remarkably, although all these tumors were classified as classical, one cluster present in all corresponded to a basal-like phenotype. These results reveal an unanticipated high heterogeneity of pancreatic cancers and demonstrate that basal-like cells, which have a highly aggressive phenotype, are more widespread than expected

Supporting decision making and early prediction of survival for oncology drug development using a pharmacometrics-machine learning based model

International audienc