Modeling the cancer transcriptome: Recent initiatives such as The Cancer Genome Atlas have mapped the genome-wide effect of individual genes on tumor growth. By unraveling genomic alterations in tumors, molecular subtypes of cancers have been identified, which is improving patient diagnostics and treatment. Uhlen et al. developed a computer-based modeling approach to examine different cancer types in nearly 8000 patients. They provide an open-access resource for exploring how the expression of specific genes influences patient survival in 17 different types of cancer. More than 900,000 patient survival profiles are available, including for tumors of colon, prostate, lung, and breast origin. This interactive data set can also be used to generate personalized patient models to predict how metabolic changes can influence tumor growth. Science , this issue p. eaan2507
Mapping the mammalian brain: The diverse physiology of the brain is reflected in its complex organization at regional, cellular, and subcellular levels. Sjöstedt et al. combined data—both newly acquired and from other large-scale brain mapping projects—from transcriptomics, single-cell genomics, in situ hybridization, and antibody-based protein profiling to map the molecular profiles in human, pig, and mouse brain. The analysis is consistent with a conserved basic brain architecture during mammalian evolution, but it does show differences in regional gene expression profiles. Science , this issue p. eaay5947
A blood cell protein-expression atlas: Genome-wide analyses are increasingly providing resources for advances in basic and applied biomedical science. Uhlen et al. performed a global expression analysis of human blood cell types and integrated this data with data across all major human tissues and organs in the human protein atlas. This comprehensive compendium allows for classification of all human protein-coding genes with regard to their tissue- and cell-type distribution. Science , this issue p. eaax9198
The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.