Jose Ramon Conejo-Garcia

This paper shows that reducing BCL11B in CD8+ T cells enhances anti-tumor response by preventing exhaustion and boosting stemness and cytotoxicity.

The paper uses patient-derived explants to study BiTE-driven T cell responses in tumors, identifying the COX/PGE2 axis as a barrier to immune activation and showing COX inhibition enhances anti-tumor effects.

The paper identifies immune phenotypes and myeloid cell networks in recurrent ovarian cancer that influence prognosis and immune environment reinstatement.

The paper examines TIL expansion in glioblastoma by analyzing spatial organization and myeloid context, finding that TIL+ tumors have distinct immune interactions and vascular niches.

CGPA provides a platform for multi-context gene-centric prognostic analysis, enabling customized exploration of gene relationships and biomarker discovery in cancer research.

This paper analyzes 11,473 tumor samples across 33 cancer types to study gamma delta T cell receptor diversity and its prognostic potential, revealing its significance as a biomarker and therapeutic target.

Cmai predicts antigen-antibody interactions from high-throughput BCR sequencing data, revealing immune responses to tumor antigens and ICI treatment outcomes.

BERLIN reanalyzes single-cell RNAseq data to identify cell types and lineages, showing accurate results in acute myeloid leukemia and tumor-reactive lymphocytes.

This paper applies wide deep learning to cell type classification, showing improved accuracy when models are trained on one cancer type and tested on another.

This study uses DNA methylation data to accurately predict immune cell infiltration in solid tumors, particularly in melanoma, and provides a simple model for clinical application.