Breast cancer quantitative proteome and proteogenomic landscape


  • In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches.
  • Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes.
  • Additionally, poor-prognosis basal-like and luminal B tumors are
    further subdivided by immune component infiltration, suggesting the current classification is incomplete.
  • Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype.


To aid in clinical implementation, a set of 50 transcripts (collectively known as PAM50) were established for the five subtypes (basal-like, HER2, luminal A & B, and normal-like) and surrogate immunohistochemistry (IHC) markers (ER, PR, HER2, and Ki67) were implemented to partially recapitulate the stratifying and prognostic information garnered in the original studies. However, multigene expression assays (e.g., MammaPrintTM, Oncotype DXTM, and Prosigna RORTM) are not readily available to all patients, and despite progress in the development of pathology-based surrogate PAM50 markers, one out of three patients are still potentially misclassified2,3.

Parallel advancements in high-throughput protein quantification techniques have enabled the burgeoning of protein-based molecular characterization of breast tumors. In theory, these classifications are a more accurate reflection of functional heterogeneity and stronger predictors of therapeutic response, as cellular function and pharmaceutical intervention are largely mediated at the protein level. Though mRNA-based classifications have had great clinical utility, certain shortcomings may be attributable to varying protein–mRNA abundance correlations4,5 and the inability of mRNA measurements to capture ligand-mediated interplay between tumor and host and characterize the extracellular space.

The immaturity of the field of high-throughput proteomics relative to transcriptomics is a major obstacle for protein-based studies to drastically alter the clinical approach to breast cancer, as Botstein et al. did nearly two decades ago. However, recent breakthroughs have offered a glimpse of that potential. High-throughput mass spectrometry-based protein quantification of PAM50 gene products was found to partially recapitulate the patient stratification offered by the original mRNA-based PAM50 subtypes5,6 and unbiased analysis of protein expression signatures has identified a subset of tumors, not identified by mRNA analysis, as being associated with a high degree of tumor differentiation and improved patient outcome5,7,8,9.


There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma.

To address this challenge, we quantitatively profiled global proteomes
and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels.

We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation.

Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic

Proteomics workflow overview