- Mass-spectrometry-based proteomic profiling of human cancers has the potential for pan-cancer analyses to identify molecular subtypes and associated pathway features that might be otherwise missed using transcriptomics
- Here, we classify 532 cancers, representing six tissue-based types (breast, colon, ovarian, renal, uterine)of the tumors, into ten proteome-based, pan-cancer subtypes that cut across tumor lineages
- Two distinct subtypes both involve the immune system, one associated with the adaptive immune response and T-cell activation, and the other associated with the humoral immune response
- Two additional subtypes each involve the tumor stroma, one of these including the collagen VI interacting network
- Three additional proteome-based subtypes—respectively involving proteins related to Golgi apparatus, hemoglobin complex, and endoplasmic reticulum—were not reflected in previous transcriptomics analyses
In this age of advanced molecular-profiling technologies, cancer molecular subtype discovery has been one of the more common exercises utilizing transcriptomic or proteomic data on human tumors. Molecular subtypes can deepen our understanding of cancer as representing a collection of diseases rather than a single disease. Molecular subtypes can provide insights into the pathways appearing deregulated within tumor subsets, which may suggest therapeutic opportunities, as well as being indicative of what pathways, as characterized in the experimental setting, would seem particularly relevant in the human disease setting
Historically, most subtype discovery studies in cancer have involved transcriptomic rather than proteomic data, as the advent of DNA microarrays over 20 years ago began the widespread use of transcriptomics among laboratories1. In contrast, proteomics is typically more challenging at a technical level and requires dedicated laboratories with the right expertise.
In a recent study, using transcriptome data by RNA sequencing (RNA-seq) from The Cancer Genome Atlas (TCGA) consortium, we classified more than 10,000 cancers, representing 32 major types, into 10 molecular-based classes that cut across tumor lineages and cancer types2.
At the same time, while protein abundance levels typically correlate with those of the corresponding mRNA, widespread discordant expression patterns between protein and mRNA are also observable3..