Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Hydrogen peroxide is appreciated as a cellular signaling molecule with second-messenger properties, yet the mechanisms by which the cell protects against intracellular H₂O₂ accumulation are not fully understood. We introduce a network model of H₂O₂ clearance that includes the pseudo-enzymatic oxidative turnover of protein thiols, the enzymatic actions of catalase, glutathione peroxidase, peroxiredoxin, and glutaredoxin, and the redox reactions of thioredoxin and glutathione. Simulations reproduced experimental observations of the rapid and transient oxidation of glutathione and the rapid, sustained oxidation of thioredoxin on exposure to extracellular H₂O₂. The model correctly predicted early oxidation profiles for the glutathione and thioredoxin redox couples across a range of initial extracellular [H₂O₂] and highlights the importance of cytoplasmic membrane permeability to the cellular defense against exogenous sources of H₂O₂. The protein oxidation profile predicted by the model suggests that approximately 10% of intracellular protein thiols react with hydrogen peroxide at substantial rates, with a majority of these proteins forming protein disulfides as opposed to protein S-glutathionylated adducts. A steady-state flux analysis predicted an unequal distribution of the intracellular anti-oxidative burden between thioredoxin-dependent and glutathione-dependent antioxidant pathways, with the former contributing the majority of the cellular antioxidant defense due to peroxiredoxins and protein disulfides. Antioxid. Redox Signal. 13, 731–743.

Preneoplastic lesions carry many of the antigenic targets found in cancer cells but often exhibit prolonged dormancy. Understanding how the host response to premalignancy is maintained and altered during malignant transformation is needed to prevent cancer. To understand the immune microenvironment in precursor monoclonal gammopathy of undetermined significance (MGUS) and myeloma, we analyzed bone marrow immune cells from 12 healthy donors and 26 patients with MGUS/myeloma by mass cytometry and concurrently profiled transcriptomes of 42,606 single immune cells from these bone marrow samples. Compared with age-matched healthy donors, memory T cells from both MGUS and myeloma patients exhibited greater terminal effector differentiation. However, memory T cells in MGUS show greater enrichment of stem-like TCF1/7 ^hi cells. Clusters of T cells with stem-like and tissue residence genes were also found to be enriched in MGUS by single-cell transcriptome analysis. Early changes in both NK and myeloid cells were also observed in MGUS. Enrichment of stem-like T cells correlated with a distinct genomic profile of myeloid cells and levels of Dickkopf-1 in bone marrow plasma. These data describe the landscape of changes in both innate and adaptive immunity in premalignancy and suggest that attrition of the bone marrow–resident T cell compartment because of loss of stem-like cells may underlie loss of immune surveillance in myeloma.

The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction–diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.