SUMMARY Hippo signaling has been recognized as a key tumor suppressor pathway. Here, we perform a comprehensive molecular characterization of 19 Hippo core genes in 9,125 tumor samples across 33 cancer types using multidimensional “omic” data from The Cancer Genome Atlas. We identify somatic drivers among Hippo genes and the related microRNA (miRNA) regulators, and using functional genomic approaches, we experimentally characterize YAP and TAZ mutation effects and miR-590 and miR-200a regulation for TAZ. Hippo pathway activity is best characterized by a YAP/TAZ transcriptional target signature of 22 genes, which shows robust prognostic power across cancer types. Our elastic-net integrated modeling further reveals cancer-type-specific pathway regulators and associated cancer drivers. Our results highlight the importance of Hippo signaling in squamous cell cancers, characterized by frequent amplification of YAP/TAZ, high expression heterogeneity, and significant prognostic patterns. This study represents a systems-biology approach to characterizing key cancer signaling pathways in the post-genomic era.

SUMMARY Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes—modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility.

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We and others have reported that the anticancer activity of L-asparaginase (ASNase) against asparagine synthetase (ASNS)-positive cell types requires ASNase glutaminase activity, whereas anticancer activity against ASNS-negative cell types does not. Here, we attempted to disentangle the relationship between asparagine metabolism, glutamine metabolism, and downstream pathways that modulate cell viability by testing the hypothesis that ASNase anticancer activity is based on asparagine depletion rather than glutamine depletion per se. We tested ASNase wild-type (ASNase WT) and its glutaminase-deficient Q59L mutant (ASNase Q59L) and found that ASNase glutaminase activity contributed to durable anticancer activity against xenografts of the ASNS-negative Sup-B15 leukemia cell line in NOD/SCID gamma mice, whereas asparaginase activity alone yielded a mere growth delay. Our findings suggest that ASNase glutaminase activity is necessary for durable, single-agent anticancer activity in vivo, even against ASNS-negative cancer types.

We present a novel method to tune the interaction potential from 5k B T to 40k B T in situ between micronsized superparamagnetic colloids. The potential is composed of a highly tunable long-range attraction induced via a rotating magnetic field and a short-range electrostatic repulsion. Various 2-D thermodynamic phases are observed in a colloidal suspension at different field strengths. Quantitative agreement is found between theory and experiments for dipolar interactions. A theory to account for the induced dipole due to neighboring particles is also presented. The effective three-body potential of a dilute trimer system is measured to account for many-body effects in the system. These results demonstrate an ideal model to study the phase behavior in 2-D systems.