Back

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.

2023-10-01 cancer biology
10.1101/2023.09.29.559755 bioRxiv
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.

50% of probability mass above

"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.