Dietmar Rieder

The paper integrates single-cell data from CRC patients to identify immune subtypes and neutrophil phenotypes, revealing their spatial organization and tumor-induced changes.

NovumRNA predicts non-canonical tumor antigens from RNA sequencing data, showing robustness across cancer types and identifying therapy-induced antigens in glioblastoma.

This study integrates multi-omic and multimodal data from IBD patients to identify molecular, histopathological, and clinical features associated with inflammatory bowel diseases.

This study shows conventional therapy in CRC induces immunoediting and changes immune contexture, with AI identifying mucin deposits as a potential surrogate marker.

This paper develops a signature, iHet, to capture tumor microenvironment heterogeneity linked to immune activity and evasion using transcriptomic data from lung cancer.

The paper assesses the immunogenic potential of GNAQ and GNA11 mutations in uveal melanoma using genomic and transcriptomic data, finding Q209L mutations are more immunogenic than Q209P.

Scirpy provides a Python toolkit for analyzing single-cell T-cell receptor data and integrating it with transcriptomic data.

quanTIseq estimates absolute fractions of ten immune cell types from bulk RNA-seq; across TCGA, CXCL9/CXCR3 expression, not mutational load, tracks CD8+ T cell infiltration.

This study shows that neutral evolution dominates tumor shaping in a mouse CRC model, and PD-1/PD-L1 blockade enhances immunoediting and tumor homogeneity.

Immunogenomic profiling of 8243 TCGA samples across 20 solid cancers yields an immune atlas and a random-forest-derived immunophenoscore stratifying melanoma responders to checkpoint blockade.