Deep spatial-omics to aid personalization of precision medicine in metastatic recurrent Head & Neck Cancers
Causer, A.; Tan, X.; Lu, X.; Moseley, P.; Teoh, M.; McGrath, M.; Kim, T.; Simpson, P.; Perry, C.; Frazer, I.; Panizza, B.; Ladwa, R.; Nguyen, Q.; Gonzalez Cruz, J. L.
Show abstract
Immune checkpoint inhibitor (ICI) modality has had a limited success (<20%) in treating metastatic recurrent Head & Neck Oropharyngeal Squamous cell carcinomas (OPSCCs). To improve response rates to ICIs, tailored approaches capable to capture the tumor complexity and dynamics of each patients disease are needed. Here, we performed advanced analyses of spatial proteogenomic technologies to demonstrate that: (i) compared to standard histopathology, spatial transcriptomics better-identified tumor cells and could specifically classify them into two different metabolic states with therapeutic implications; (ii) our new method (Spatial Proteomics-informed cell deconvolution method or SPiD) improved profiling of local immune cell types relevant to disease progression, (iii) identified clinically relevant alternative treatments and a rational explanation for checkpoint inhibitor therapy failure through comparative analysis of pre- and post-failure tumor data and, (iv) discovered ligand-receptor interactions as potential lead targets for personalized drug treatments. Our work establishes a clear path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Single-Cell RNA Sequencing Reveals the Effects of Chemotherapy on Human Pancreatic Adenocarcinoma and its Tumor Microenvironment 97%
- Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates 96%
- Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma 96%
Similar papers in this journal
- Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images 97%
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma 95%
- Single-Cell Spatial Proteomics Analyses of Head and Neck Squamous Cell Carcinoma Reveal Tumor Heterogeneity and Immune Architectures Associated with Clinical Outcome 95%
Similar papers in this journal
- An Omic and Multidimensional Spatial Atlas from Serial Biopsies of an Evolving Metastatic Breast Cancer 97%
- Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes 97%
- Systematic annotation of orphan RNAs reveals blood-accessible molecular barcodes of cancer identity and cancer-emergent oncogenic drivers 96%
Similar papers in this journal
- Cancer-associated fibroblast compositions change with breast cancer progression linking S100A4 and PDPN ratios with clinical outcome 96%
- Metastasis founder cells activate immunosuppression early in human melanoma metastatic colonization 94%
- Negative trade-off between neoantigen repertoire breadth and the specificity of HLA-I molecules shapes antitumour immunity 94%
Similar papers in this journal
- Evaluating the transcriptional fidelity of cancer models 95%
- Multimodal integration of single cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations 95%
- Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seq 95%
"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.