Biology subjects

Mayfield, J.

Publications and source records attributed to Mayfield, J..

3 recordsLinked to original sources

bioRxiv2026

Gene coordination patterns across 8,314 tumors reveal a spectral point of no return in cancer progression

Genes operate through coordinated patterns that preserve tissue self-identity, regulate immunity, and control growth. We hypothesize that this coordination undergoes a structured collapse during cancer progression and that there is a shared point of no return across multiple cancer types that may help predict patient outcomes. We calculated gene-gene coordination across 8,314 tumors spanning 32 cancer types by measuring how similarly each pair of 3,000 genes behaves across the patient population. We find that 71% of all coordination is captured by a single pattern, with three secondary patterns that predict survival in 11 of 30 cancer types including patterns involving metabolic dedifferentiation (cytochrome P450 metabolism vs. epithelial differentiation), immune polarization (adaptive vs. innate immunity), and tissue selfidentity. Specifically, this method stratifies prognosis in glioblastoma (C-index 0.845, p = 1.8 x 10-6), prostate adenocarcinoma (0.800), clear cell renal carcinoma (0.649), and eight additional cancers (p = 6.9 x 10-32). Beyond this threshold, tumors of all types share uniformly poor survival, defining a universal molecular point of no return and a potential window for early intervention before coordination collapse becomes irreversible.

cancer biology↗

bioRxiv2026

A Proteome-Calibrated Mechanistic Digital Twin of the Glioblastoma Astrocyte Derived from Patient Multi-Omic Data

Digital twins of biological systems have been proposed as a framework for personalized medicine; however, existing implementations in oncology operate at the tissue or population scale with parameters derived from textbook kinetics rather than patient data. We describe a mechanistic digital twin of the glioblastoma (GBM) astrocyte in which all rate constants are calibrated to proteomic measurements from 50 wild-type GBM patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC, BCM source, n = 99 total). The twin comprises a 25-node ordinary differential equation (ODE) signaling network spanning the EGFR-MAPK, PI3K-AKT-mTOR, JAK-STAT, and cell cycle axes, a Hamiltonian covariance matrix constructed from 9,358 proteomic features (spectral concentration c = 0.168, 80% variance in 24 modes), and a gradient-based algebraic steady-state calibration procedure that achieves Spearman{rho} = 1.000 between simulated and measured protein abundances across 10 observable signaling nodes in 3.6 seconds on an A100 GPU. Five clinically relevant genetic perturbations were implemented (EGFRvIII, PTEN loss, IDH1 R132H, PD-L1 upregulation, compound EGFRvIII + PTEN loss), and four drug classes were simulated (erlotinib, temsirolimus, atezolizumab, ivosidenib) at multiple doses. All six pre-specified biological validation checks passed, including PTEN-loss AKT escape from erlotinib via basal PI3K activity. Erlotinib dose-response curves reveal that EGFRvIII cells maintain pERK activity until approximately 1 {micro}M before collapsing, while PTEN-loss cells exhibit deeper suppression because the EGFR-MAPK axis becomes the sole remaining survival signal when PI3K is constitutively active. Perturbation directionality is mechanistically coherent across all genotype-drug combinations. The framework demonstrates that proteome-calibrated mechanistic cell digital twins are computationally tractable, biologically interpretable, and distinguishable from existing population-level statistical models in both their construction and their limitations. Code is available at https://github.com/radres2019/gbm-digital-twin.

synthetic biology↗

bioRxiv2023

FBP1 is a nonenzymatic safety valve that curtails AKT activation to prevent insulin hyperresponsiveness

Insulin inhibits gluconeogenesis and stimulates glucose conversion to glycogen and lipids. How these activities are coordinated to prevent hypoglycemia and hepatosteatosis is not clear. Fructose-1,6 bisphosphatase (FBP1) is rate controlling for gluconeogenesis. However, inborn human FBP1 deficiency does not cause hypoglycemia unless accompanied by fasting or starvation, which also trigger paradoxical hepatomegaly, hepatosteatosis, and hyperlipidemia in affected individuals. Hepatocyte FBP1-ablated mice exhibit identical fasting-conditional pathologies along with AKT hyperactivation, whose inhibition reversed hepatomegaly, hepatosteatosis and hyperlipidemia but not hypoglycemia. Surprisingly, fasting-mediated AKT hyperactivation is insulin-dependent. FBP1 prevents insulin hyperresponsiveness, independently of its catalytic activity, by interacting with AKT, PP2A-C and Aldolase-B (ALDOB) to specifically accelerate AKT dephosphorylation. Enhanced by fasting and weakened by elevated insulin, FBP1:AKT:PP2A-C:ALDOB complex formation, which is disrupted by human FBP1 deficiency mutations or a C-terminal FBP1 truncation, prevents insulin-triggered liver pathologies and maintains lipid and glucose homeostasis. Conversely, a complex disrupting peptide reverses diet-induced insulin resistance.

molecular biology↗