Traditional 2D cancer cell lines often fail to accurately model primary cancer cells, hindering the translation of in vitro research to the clinic. An emerging solution is the use of patient tissue-derived cells expanded in 3D, known as cancer organoids or tumoroids. In this study, we performed a comprehensive proteomic analysis of patient-derived tumoroids using the Thermo Scientific™ Orbitrap™ Astral™ Mass Spectrometer.

Tumoroids were first established from dissociated cells from patient tumor resections using Gibco™ OncoPro™ Tumoroid Culture Medium, and have previously been shown to maintain strong concordance in gene expression with primary tumor material. However, little has been done to investigate protein-level expression patterns in these patient-derived models. The proteomic profiles, generated from over 10,000 unique proteins identified in each sample, revealed significant insights into the signaling pathways associated with breast cancer, specifically MAPK, ALK, and ERBB2 pathways, as well as protein and RNA metabolism.

In comparing transcriptomic and proteomic datasets, particularly where expression varied across tumoroid models, there was significant overlap in terms of ECM and adhesion-related proteins, as well as lipid metabolism. This overlap underscores the importance of these proteins in the biological processes of cancer and emphasizes the value of integrating proteomic and transcriptomic data to achieve a more comprehensive understanding of tumoroid biology. While transcriptomics provides a snapshot of gene expression, proteomics offers a deeper understanding of the functional state of the cell by identifying post-translational modifications and protein interactions. This multi-omics approach enhances our ability to model the complex heterogeneity of cancer and supports the development of personalized treatment strategies.

Future work will focus on validating these proteomic findings, including making protein-level comparisons between established tumoroids and primary tissue, and exploring post-translational modifications. Taken together, these new insights are expected to further improve the efficacy of tumoroid models in precision oncology and drug discovery.