The cellular composition of the tumor microenvironment is essential for diagnosis, prognosis, and treatment selection in precision oncology. Computational deconvolution of bulk omics data can infer cell type proportions from mixed samples without physical cell sorting. While numerous deconvolution tools exist for transcriptomics, very few target bulk proteomics data. The distinct nature of proteomic data raises the question whether transcriptomics-derived tools can be reliably applied to proteomics without adaptation.

We conducted a multi-dimensional benchmarking study to evaluate the applicability of existing deconvolution methods to bulk proteomics data. Using two public immune cell proteomics, we generated synthetic bulk mixtures with known cell type proportions in different mixture composition settings. Our benchmark evaluates six deconvolution algorithms using different imputation strategies for missing values and various signature matrix construction methods.

Our benchmarking reveals a substantial performance degradation of all algorithms when moving from idealistic to more realistic simulation settings, indicating that published benchmarks may overestimate real-world accuracy. Second, the optimal algorithm choice depends on the combination of preprocessing and experimental scenario. Third, we observe systematic cell-type-specific biases showing as consistent over- or underestimation of individual cell types. Fourth, small signatures based on well-characterized marker proteins may be more robust than larger data-driven signature matrices. Finally, unsupervised methods demonstrate limited reliability across the range of tested conditions.

Our findings motivate both the development of dedicated, proteomics-aware deconvolution tools and the creation of experimentally validated benchmark datasets to enable robust immune cell profiling in precision oncology.

Co-authors: Ludovica Sibilia, Mira Herold, Samuel Gair, Marta Pinto Carbo, Lopamudra Chatterjee, Nadia Djerbi, Dorothea Rutishauser, Rosary Yao, Thi Huong Lan Do, Marco Bühler, Christoph Messner, Thorsten Zenz, Valentina Boeva