easier: interpretable predictions of antitumor immune response from bulk RNA-seq data
Lapuente-Santana, O.; Marini, F.; Ustjanzew, A.; Finotello, F.; Eduati, F.
Show abstract
Immunotherapy with immune checkpoint blockers (ICB) is associated with striking clinical success, but only in a small fraction of patients. Thus, we need computational biomarker-based methods that can anticipate which patients will respond to treatment. Current established biomarkers are imperfect due to their incomplete view of the tumor and its microenvironment. We have recently presented a novel approach that integrates transcriptomics data with biological knowledge to study tumors at a more holistic level. Validated in four different solid cancers, our approach outperformed the state-of-the-art methods to predict response to ICB. Here, we introduce estimate systems immune response (easier), an R/Bioconductor package that applies our approach to quantify biomarkers and assess patients likelihood to respond to immunotherapy, providing just the patients baseline bulk-tumor RNA-sequencing (RNA-seq) data as input.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- tugMedi: simulator of cancer-cell evolution for personalized medicine based on the genomic data of patients 91%
- Identification of factors mediating the signaling convergenceof multiple receptors following cell-cell interaction 91%
- Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma 91%
Similar papers in this journal
- A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes 92%
- Automated cell type annotation and exploration of single-cell signalling dynamics using mass cytometry 92%
- Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients. 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.