Histopathology-assisted proteogenomics provides foundations for stratification of melanoma metastases
Kuras, M.; Betancourt, L. H.; Hong, R.; Szadai, L.; Rodriguez, J.; Horvatovich, P.; Pla, I.; Eriksson, J.; Szeitz, B.; Deszcz, B.; Welinder, C.; Sugihara, Y.; Ekedahl, H.; Baldetorp, B.; Ingvar, C.; Lundgren, L.; Lindberg, H.; Oskolas, H.; Horvath, Z.; Rezeli, M.; Gil, J.; Appelqvist, R.; Kemeny, L. V.; Malm, J.; Sanchez, A.; Szasz, A. M.; Pawlowski, K.; Wieslander, E.; Fenyo, D.; Nemeth, I. B.; Marko-Varga, G.
Show abstract
Here we describe the histopathology-driven proteogenomic landscape of 142 treatment-naive metastatic melanoma samples. We identified five proteomic subtypes that integrate the immune and stroma microenvironment components, and associate with clinical and histopathological parameters, providing foundations for an in-depth molecular classification of melanoma. Our study shows that BRAF V600 mutated melanomas display heterogeneous biology, where the presence of an oncogene-induced senescence-like phenotype improves patient survival. Therefore, we propose a mortality-risk-based stratification, which may contribute to a more personalized approach to patient treatment. We also found a strong association between tumor microenvironment composition, disease progression, and patient outcome supported by single-cell omic signatures that point to straightforward histopathological connective tissue-to-tumor ratio assessment for better informed medical decisions. A melanoma-associated signature of single amino acid variants (SAAV) responsible for remodeling the extracellular matrix was uncovered together with SAAV-derived neoantigen candidates as targets of anti-tumor immune responses. Overall, this study offers comprehensive stratifications of melanoma metastases that may help develop tailored strategies for diagnosing and treating the disease.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Cellular-level phenotyping of tumor-immune microenvironment (TiME) in patients in vivo reveals distinct inflammation and endothelial anergy signatures 95%
- Cancer associated fibroblast subtypes modulate the tumor-immune microenvironment and are associated with skin cancer malignancy 95%
- Melanoblast transcriptome analysis reveals novel pathways promoting melanoma metastasis 95%
Similar papers in this journal
- Spatial transcriptomics analysis identifies a unique tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease 94%
- Conserved angio-immune subtypes of the cancer microenvironment predict response to immune checkpoint blockade therapy 94%
- TimiGP: inferring inter-cell functional interactions and clinical values in the tumor immune microenvironment through gene pairs 94%
Similar papers in this journal
- Multiplexed Imaging Analysis of the Tumor-Immune Microenvironment Reveals Predictors of Outcome in Triple-Negative Breast Cancer 94%
- Functional analysis of recurrent non-coding variants in human melanoma 94%
- Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals an invasive tumor vasculature phenotype 94%
Similar papers in this journal
- Quantitative analysis of tyrosine phosphorylation from FFPE tissues reveals patient specific signaling networks 95%
- BRAF inhibitors reprogram cancer-associated fibroblasts to drive matrix remodeling and therapeutic escape in melanoma 94%
- High-density, targeted monitoring of tyrosine phosphorylation reveals activated signaling networks in human tumors 94%
Similar papers in this journal
- Genomic Insights Guiding Personalized First-Line Immunotherapy Response in Lung and Bladder Tumors 93%
- Three Conserved Immune Dysfunction and Exclusion Subtypes in Bladder and Pan-cancers: Prognostic and Immunotherapeutic Significance 92%
- A live tumor fragment platform to assess immunotherapy response in core needle biopsies while addressing challenges of tumor heterogeneity 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.