Toward real clinical utility: leveraging comprehensive cancer proteomic datasets for clinical insight

Recent advances in multi-omic technologies and computational tools have enabled comprehensive studies of cancer that integrate proteomics, genomics, transcriptomics, and metabolomics to improve disease understanding and outcomes.

1. Recent improvements in throughput and decreasing sample mass requirements have enabled deep analysis of hundreds of human samples in multi-omic studies, increasing the statistical rigor of these studies and facilitating comparisons across clinical and demographic categories.2. Despite advances in statistical modeling, machine learning, and pathway-aware analysis, the principal outcome from these observational studies remains correlational-strong statistical associations between omic features and clinical characteristics, including clinical outcomes.3. Demonstration of causal relationships requires multi-pronged mechanistic experiments involving techniques in molecular and cellular biology that are distinct from the analytical and computational skills needed to generate these datasets.

National Library of Medicine PubMed database.

True clinical utility depends on the demonstration of causal relationships between candidate targets and the biomedical process of interest. Enhanced collaboration with molecular and cellular biologists skilled in the use of modern tools of genetic manipulation and engineered model systems is required to realize the full translational potential of even the most comprehensive multi-omic studies.