Aurélien de Reyniès

fastCNV R package for scRNA-seq and spatial transcriptomics data

Gadea Cabrejas, Clarice S. Groeneveld, Aurélien de Reyniès

fr (code pays fourni par la source)

fastCNV R package for scRNA-seq and spatial transcriptomics data

Gadea Cabrejas, Clarice S. Groeneveld, Aurélien de Reyniès

fr (code pays fourni par la source)

Supplemental Figure 5 from Transcriptomic Signatures of MSI-High Metastatic Colorectal Cancer Predict Efficacy of Immune Checkpoint Inhibitors

Claire Gallois, Matteo Landi, Julien Taı̈eb, Marine Sroussi et autres

Kaplan Meier curves for progression-free survival according to the three unsupervised clusters in the validation cohort

Supplemental Figure 4 from Transcriptomic Signatures of MSI-High Metastatic Colorectal Cancer Predict Efficacy of Immune Checkpoint Inhibitors

Claire Gallois, Matteo Landi, Julien Taı̈eb, Marine Sroussi et autres

Heatmap with unsupervised clustering in the validation cohort

Supplemental Figure 2 from Transcriptomic Signatures of MSI-High Metastatic Colorectal Cancer Predict Efficacy of Immune Checkpoint Inhibitors

Claire Gallois, Matteo Landi, Julien Taı̈eb, Marine Sroussi et autres

Kaplan Meier curves for progression-free survival according to the unsupervised clusters and the line of immune checkpoint inhibitor

Supplemental Figure 6 from Transcriptomic Signatures of MSI-High Metastatic Colorectal Cancer Predict Efficacy of Immune Checkpoint Inhibitors

Claire Gallois, Matteo Landi, Julien Taı̈eb, Marine Sroussi et autres

Survival after recurrence in MSI patients from PETACC8 trial with available 3’RNAseq data

Supplemental Figure 1 from Transcriptomic Signatures of MSI-High Metastatic Colorectal Cancer Predict Efficacy of Immune Checkpoint Inhibitors

Claire Gallois, Matteo Landi, Julien Taı̈eb, Marine Sroussi et autres

Selection of the transcriptomic signatures for the unsupervised clustering

Supplemental Figure 3 from Transcriptomic Signatures of MSI-High Metastatic Colorectal Cancer Predict Efficacy of Immune Checkpoint Inhibitors

Claire Gallois, Matteo Landi, Julien Taı̈eb, Marine Sroussi et autres

Tumor mutational burden and diagnosed Lynch syndrome in patients with MSI-high status determined by only one method

Deep Learning Bridges Histology and Transcriptomics to Predict Molecular Subtypes and Outcomes in Muscle-Invasive Bladder Cancer

Alice Blondel, Clémentine Krucker, Valentin Harter, Melissa Da Silva et autres

Abstract Muscle-Invasive Bladder Cancer (MIBC) is a heterogeneous disease with distinct molecular subtypes influencing prognosis and therapeutic response. However, molecular profiling through RNA sequencing remains costly, time-consuming and complicated by intratumoral heterogeneity. We developed a Deep Learning (DL) approach to infer molecular …

fr, lu, us (code pays fourni par la source)

Integrative genomic profiling of large-cell neuroendocrine carcinomas reveals distinct subtypes of high-grade neuroendocrine lung tumors

James McKay, Florian Malchers, Jörg Sänger, Odd Terje Brustugun et autres

us (code pays fourni par la source)

Deep Learning on Histologic Slides Accurately Predicts Consensus Molecular Subtypes and Spatial Heterogeneity in Colon Cancer

Jean-Eudes Le Douget, Paul Jacob, Côme Lepage, Claire Gallois et autres

fr (code pays fourni par la source)

Improved tumor-type informed compared to tumor-informed mutation tracking for ctDNA detection and microscopic residual disease assessment in epithelial ovarian cancer

Henri Azaïs, Charles Marcaillou, Sylvain Guibert, Emmanuel Martin et autres

BACKGROUND: Epithelial ovarian cancer (EOC) is a leading cause of cancer mortality in women, often diagnosed at advanced stages. While first-line treatments improve survival, relapses remain common, with 5-year survival rates below 40%. Circulating tumor DNA (ctDNA) is a promising biomarker for …

fr (code pays fourni par la source)