Jae-Ho Cheong

Mitochondrial Complex I generates reactive oxygen species at its flavin cofactor, where spin-dependent reaction pathways have been proposed. Models of this chemistry treat the flavin– oxygen radical pair in isolation, although the flavin is wired to a chain of eight iron–sulfur clusters. Here we model the pair with this chain using the human Complex I structure and open-quantum-system models of spin coupling and electron transfer. In the simulated network, spin coherence decays rapidly along the chain, whereas proximal clusters alter the calculated radical-pair singlet yield. Their influence is kinetically gated: a downstream cluster affects the modelled yield when its electron-transfer rate competes with radical-pair recombination, a threshold robust to driving force and to the transfer model. Modelling does set the sign: one-directional spin hops predict suppression and inflate coherence transport, whereas charge-resolved transfer drives the singlet yield towards its spin-randomized limit unless triplet recombination dominates. Detectable spin-dependent effects in this framework require superoxide spin relaxation to be substantially slower than in free solution, a condition not yet established for Complex I.

Wnt proteins regulate adult tissue homeostasis and repair by driving stem cell self-renewal and differentiation. High-performance Wnt preparations have enormous therapeutic potential, especially alongside various stem cell technologies. Currently, a major limitation of most Wnt preparations is that they inevitably require fetal bovine serum (FBS) or detergents to maintain Wnt solubility and activity. Recently, afamin (AFM) was identified as a serum factor that solubilizes Wnt3a in conditioned media (CM), obviating the requirement for animal sera. Here, we show that serum albumin (SA) helps maintain the activity and stability of AFM-solubilized Wnt3a in CM, likely through a stabilizing effect. We further demonstrate, using test-tube assays, that SA alone efficiently solubilizes purified Wnt3a (pWnt3a) independently of AFM. This approach enables the conversion of conventional detergent-solubilized pWnt3a (CHAPS–pWnt3a) into SA–pWnt3a through dialysis-mediated detergent removal. Together, these findings define a simple and robust SA-based strategy for preserving Wnt3a activity, addressing a long-standing challenge in both experimental and therapeutic settings.

Mitochondrial electron transfer depends on quantum-mechanical coupling between redox cofactors, but its evolutionary constraints in cancer remain unclear. We combined the human complex III cryo-EM structure, Pathways calculations and Marcus theory to predict how missense substitutions in mitochondrial cytochrome b (MT-CYB), which coordinates the Q-cycle haem centres, affect electron-transfer kinetics. Comparing 524 somatic mutations from TCGA and PCAWG with 597 germline variant sites, we found that charge-changing substitutions near the haems were depleted in the germline (observed/expected, 0.12) but enriched in tumours (1.81). Greater predicted kinetic perturbations corresponded to stronger germline depletion and tumour enrichment, predominantly at low heteroplasmy and independently of tumour purity. Predicted kinetic effects correlated strongly with electrostatic perturbations (r = 0.94), precluding identification of tunnelling-specific selection. These findings support relaxed purifying selection on MT-CYB variants predicted to disrupt mitochondrial electron transfer during the evolution of diverse human cancers, without demonstrating adaptive benefits or selection on tunnelling itself.

Cancer multi-omics faces challenges in handling the scale, complexity, and heterogeneity of multi-omics data, limiting progress in variant interpretation, tumor classification, and modeling cancer evolution. Quantum computing offers a new paradigm using superposition, entanglement, and quantum interference to efficiently explore vast solution spaces. This Perspective highlights how quantum algorithms-such as Quantum Support Vector Machines, Quantum Principal Component Analysis, and quantum generative models-could enhance key tasks in precision oncology, including multi-omics integration, spatial transcriptomics, and neoantigen prediction. Current technical barriers, like qubit noise and limited quantum memory, are discussed alongside strategies to connect quantum computing with biomedical research. Interdisciplinary collaboration will be essential to realizing quantum advantage in cancer multi-omics

Background: Quantum biology explores how quantum mechanical phenomena—including coherence, tunneling, superposition, and spin dynamics—contribute to biological function. Although once considered negligible in warm and noisy biological environments, increasing evidence suggests that quantum effects play important roles in diverse living systems. Objective: This review aims to summarize the current understanding of quantum biological mechanisms, highlight their relevance to physiology and disease, and discuss emerging biomedical and technological applications. Methods: We reviewed recent experimental, computational, and theoretical advances in quantum biology, including studies employing ultrafast spectroscopy, quantum sensing, cryo‐electron microscopy, and quantum simulation approaches. Key biological systems examined include photosynthetic complexes, enzymatic reactions, DNA base pairing, sensory systems, and mitochondrial electron transport. Results: Accumulating evidence indicates that quantum coherence, tunneling, and spin‐dependent processes contribute to photosynthetic energy transfer, enzymatic catalysis, proton transfer in DNA, magnetoreception, olfaction, and mitochondrial bioenergetics. Advances in quantum sensing and computational modeling have further enabled direct investigation of coherence dynamics and electron transfer mechanisms in biological systems. These findings suggest that quantum effects may influence aging, cancer, neurodegeneration, and metabolic dysfunction through mechanisms involving reactive oxygen species production, mutagenesis, and altered redox signaling. Conclusion: Quantum biology is evolving from a speculative concept into an experimentally accessible and translationally relevant discipline. Integrating quantum principles with systems biology, multi‐omics, and precision medicine may provide new opportunities for diagnostics, biomarker discovery, and therapeutic development. Continued advances in spectroscopy, quantum sensing, and quantum computing are expected to further establish the role of quantum phenomena in health and disease.

Background: Key biological processes underlying health and disease‐including electron transfer, redox regulation, and radical‐mediated signaling‐are fundamentally governed by quantum‐mechanical principles. These processes are central to mitochondrial function, metabolism, and cellular signaling, yet their biomedical implications have remained difficult to address using classical computational approaches. Rationale: Recent advances in quantum computing, quantum sensing, and quantum machine learning enable direct simulation and measurement of quantum phenomena in biologically relevant systems. Hybrid quantum‐classical algorithms, such as the Variational Quantum Eigensolver and Quantum Phase Estimation, now provide first‐principles access to redox potentials, electronic couplings, and spin‐dependent reactions that are directly linked to disease mechanisms. These developments establish the foundation for quantum biomedicine as a translational framework bridging molecular physics and clinical medicine. Content: This review synthesizes current progress in the application of quantum technologies to biomedicine, emphasizing translational relevance. We discuss quantum‐informed modeling of cancer metabolism and redox rewiring, protein misfolding in neurodegenerative diseases, immune and inflammatory signaling, infectious disease mechanisms, and drug discovery. We further propose a Quantum‐Experimental‐Clinical (QEC) pipeline that integrates quantum simulations with experimental validation and multi‐omics clinical data, enabling mechanistic interpretation of disease phenotypes and identification of redox‐ and spin‐sensitive therapeutic targets. Conclusion: Quantum biomedicine introduces a new mechanistic layer that links electronic‐scale processes to clinical phenotypes. While current implementations are constrained by NISQ‐era hardware, rapid advances in quantum algorithms and sensing technologies position quantum approaches as emerging tools in precision and translational medicine. Strategic integration of quantum methods with experimental and clinical workflows may accelerate biomarker discovery and therapeutic development. Key points: Quantum biomedicine redefines life as a dynamic equilibrium sustained by quantum coherence, tunnelling and redox resonance. Hybrid quantum–classical algorithms, such as VQE and QPE, enable first‐principles modelling of redox and spin‐dependent reactions with near‐experimental accuracy. NISQ‐era hardware supports proof‐of‐concept simulations of electron tunnelling and radical‐pair dynamics, bridging computation with measurable biophysics. Integration of quantum simulations with spectroscopy and cryo‐EM establishes a quantum–experimental–clinical (QEC) pipeline linking theory, experiment and medicine. Ethical, educational and governance frameworks are essential for equitable, transparent and sustainable implementation of quantum health technologies.

Background: WNT signaling plays a key role in maintaining the gastric epithelium and promoting tumorigenesis. However, how gastric tumors achieve WNT niche independence remains unclear, as mutations on APC or CTNNB1 —common mechanisms of ligand-independent WNT activation in colorectal cancer—are infrequent in gastric cancer. Understanding how WNT self-sufficiency is acquired in the stomach is therefore critical. Methods: We analyzed mouse gastric organoids harboring oncogenic KRAS ^G12D with or without RNF43/ZNRF3 (RZ) or CDH1/TP53 (CP) mutations, along with corresponding in vivo mouse models. Niche independence was assessed through growth factor withdrawal, Porcupine and pathway-specific inhibitor treatments, and WNT rescue assays. We performed single-nucleus multiome sequencing (RNA + ATAC) to investigate transcriptional and chromatin dynamics. Findings from mouse models were validated using patient-derived gastric cancer organoids, and pan-cancer cell line datasets were analyzed to evaluate clinical and cross-tissue relevance. Results: Gastric fibroblasts secreted canonical WNT2B to maintain the homeostatic gastric epithelium. Upon KRAS activation, epithelial cells were reprogrammed to secrete WNT ligands independently of additional mutations. Single-nucleus multiome analysis revealed that KRAS-driven MAPK signaling opened SMAD2/3-bound enhancers at the WNT7B locus, leading to the emergence of WNT7B -expressing subpopulations. Inhibition of SMAD2/3 phosphorylation suppressed both organoid growth and WNT7B transcription, whereas exogenous WNT restored organoid proliferation. Patient-derived organoids with HER2 amplification, KRAS amplification, or WNT2 copy-number gain exhibited Porcupine inhibitor-sensitive growth, indicating dependence on WNT secretion from the organoids. Analysis of public transcriptomic datasets further demonstrated that the KRAS–MAPK–WNT7B axis is conserved across other cancer types, including lung cancer. Conclusions: Gastric tumors can bypass niche dependence by acquiring KRAS–MAPK–SMAD2/3-driven epithelial WNT secretion. Targeting this axis—through MAPK inhibition, SMAD2/3 blockade, or suppression of WNT secretion—may represent a therapeutic vulnerability in gastric cancer and other KRAS-high malignancies.

Supplementary Figure 1. ACTA2 expression was elevated in the molecular subtype with the worst prognosis (Group 4). Supplementary Figure 2. ACTA2 expression stratified by microsatellite stability status in the pooled cohort. Supplementary Figure 3. Alternatively tested thresholds to stratify patients into ACTA2-High and ACTA2-Low groups.Supplementary Figure 4. ACTA2 expression stratified by microsatellite stability status in the immune checkpoint inhibitor cohort.

Supplementary Table 1. Univariable associations of the multivariable Cox proportional hazards model in the Yonsei cohort.Supplementary Table 2. Clinical characteristics of patients in the pooled validation cohort.Supplementary Table 3. Univariable associations of the multivariable Cox proportional hazards model of the pooled validation cohort.Supplementary Table 4. Univariable Cox proportional hazards regression of the association of individual genes of the 32-gene signature and their association with overall survival in the Yonsei cohort.Supplementary Table 5. Univariable Cox proportional hazards regression of the association of individual genes of the 32-gene signature and their association with overall survival in the pooled cohort.Supplementary Table 6. Multivariable analysis of the pooled cohort including microsatellite stability status.Supplementary Table 7. Multivariable analysis of the association of ACTA2 expression with recurrence-free survival in the pooled cohort.Supplementary Table 8. Clinical characteristics of patients treated with immune checkpoint inhibitors.Supplementary Table 9. ACTA2 expression in cells analyzed by single nuclear RNA sequencing cohort.Supplementary Table 10. Characteristics of patients analzyed by digital spatial transcriptomics.