Daechan Park

Stable secondary structures are important for aptamer folding and target recognition, but they can introduce structure-dependent amplification bias during Systematic Evolution of Ligands by Exponential Enrichment (SELEX), with G-quadruplexes (G4s) representing the most demanding case. Here, we introduce strand-displacement SELEX (SD-SELEX), which employs a polymerase with robust 5′→3′ strand-displacement activity to improve the amplification of structured templates. Comparative analyses showed that SD polymerase efficiently amplified hairpin- and G4-forming templates, whereas Taq polymerase exhibited structure-dependent amplification variability. In parallel thrombin SELEX experiments, SD-SELEX achieved 37-fold greater structural preservation and 65-fold higher batch-to-batch reproducibility than Taq-SELEX over 11 rounds. High-throughput sequencing further showed that SD-SELEX maintained G4 motif representation and progressively enriched the known 15-mer thrombin-binding aptamer through round 8, after which it was gradually replaced by newly identified G4 variants, whereas Taq-SELEX progressively depleted these sequences. SD-SELEX also identified novel G4 aptamers with affinities comparable to that of the reference aptamer, whereas representative Taq-SELEX candidates generally showed weaker binding. A parallel selection against VEGF-165 showed the same divergence, with SD-SELEX retaining structurally complex sequences and a previously reported functional motif that were progressively lost under Taq. Thus, SD-SELEX primarily improves aptamer discovery by reducing structure-induced amplification bias and preserving structurally diverse functional candidates, rather than by substantially increasing affinity alone.

Chimeric antigen receptor (CAR) cell therapies have transformed haematological cancer treatment but remain largely ineffective against desmoplastic solid tumours, where a fibroblast-rich matrix restricts effector cell infiltration. Depleting that stroma requires repeated clearance of abundant fibroblasts, yet CAR binders are still commonly selected and optimized around equilibrium affinity. Here we show that how quickly a CAR releases antigen after killing, rather than how tightly it binds, governs repetitive stromal targeting. We identified endoglin (CD105) on the abundant fibroblasts organizing this barrier in pancreatic cancer, then screened CD105 binders in matched CARs. The lead binder bound 40-fold more weakly than the reference antibody yet killed fibroblasts far more effectively, dissociating 11-fold faster. The difference lay after cytolysis: killing was equally fast, release was not, and faster release reset the synapse, reduced antigen trogocytosis and sustained serial killing. In fibroblast-rich tumours, a humanized variant opened a window for mesothelin-directed CAR-T and CAR-NK cells. Release kinetics therefore determine whether a CAR cell works as a reusable stromal-remodelling effector, and tuning them may improve any CAR that must clear abundant targets.

Therapeutic resistance and disease recurrence remain major unresolved challenges in oncology, primarily driven by tumor heterogeneity and the inherent plasticity of cancer cells. Although multiple biological mechanisms contribute to these processes, epigenetic mechanisms are the key regulators of clonal diversification and adaptive transcriptional reprogramming under treatment pressure. This regulatory layer operates through reversible transcriptional changes that are independent of DNA sequence alterations, enabling cancer cells to respond to a selective environment. Recent advances in analytical methodologies, particularly single‐cell multi‐omics approaches, have markedly improved our capacity to dissect these regulatory processes at a single‐cell resolution. This review explores how diverse therapeutics, including chemotherapy, targeted agents, immunotherapy, hormonal interventions, and epigenetic drugs, induce the widespread remodeling of DNA methylation patterns, histone modifications, and chromatin accessibility. These therapy‐induced molecular changes drive transitions to distinct cellular states that confer survival advantages such as drug‐tolerant persister (DTP) phenotypes, senescence‐like populations, epithelial–mesenchymal transition (EMT) states, and immune‐evasive cell populations. We further evaluated the current single‐cell multi‐omics platforms for profiling chromatin‐based plasticity and identifying biomarkers with direct clinical relevance. Finally, we discuss how integrative multi‐layer analyses enable comprehensive characterization of tumor‐state evolution, providing a conceptual framework for precision oncology strategies aimed at overcoming resistance.

Rationale: Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells represent a promising modality for cancer immunotherapy, yet their efficacy in solid tumors is limited by immunosuppressive cues from the tumor microenvironment (TME), particularly, transforming growth factor β (TGFβ).

Chang Beom Jeong, Hyein Cho & Daechan Park. BMB Reports -0001;0:. https://doi.org/10.5483/BMBRep.2025-0020

We aimed to identify the transcriptomic signatures of soft tissue sarcoma (STS) related to radioresistance and establish a model to predict radioresistance. Nine STS cell lines were cultured. Adenosine triphosphate-based viability was determined 5 days after irradiation with 8 Gy of X-rays in a single fraction. Radiosensitive and radioresistant groups were stratified according to the survival rates. Whole transcriptomic sequencing analysis was performed and differentially expressed genes (DEGs) were identified between the radiosensitive and radioresistant groups. For model generation, a cohort of 59 patients with sarcomas from The Cancer Genome Atlas (TCGA) was used. DEGs of the responder and non-responder groups according to the radiotherapy-best response were identified. The overlapping DEGs between those from TCGA data and the STS cell line were subjected to linear regression to develop a formula, namely the STS-specific radioresistance index (STS-RRI), and its performance was compared with that of the previously established radiosensitivity index (RSI). We selected thirteen overlapping DEGs and established STS-RRI using seven of them: STS-RRI = 1.5185 × MYO16-0.01575 × MYH11 + 3.900375 × KCTD16 + 0.105375 × SYNPO2-0.777375 × MYPN-0.849875 × PCSK6-0.700125 × LTK + 39.4635. Delong's test revealed that the STS-RRI performed better at stratifying responder and non-responder in TCGA cohort than the RSI (p = .002). The progression-free survival curves of the TCGA cohort were significantly discriminated by STS-RRI (p = .013) but not by RSI (p = .241). We developed the STS-RRI to predict the radioresistance of patients with STS in the TCGA dataset, showing a higher performance than RSI.

Most cancer mutation profiling studies are laboratory-based and lack direct clinical application. For clinical use, it is necessary to focus on key genes and integrate them with relevant clinical variables. We aimed to evaluate the prognostic value of the dosage of the KRAS G12 mutation, a key pancreatic ductal adenocarcinoma (PDAC) variant and to investigate the biological mechanism of the prognosis associated with the dosage of the KRAS G12 mutation. In this retrospective cohort study, we analyzed 193 surgically treated patients with PDAC between 2009 and 2016. RNA, whole-exome, and KRAS-targeted sequencing data were used to estimate the dosage of the KRAS G12 mutant. Our prognostic scoring system included the mutation dosage from targeted sequencing ( > 0.195, 1 point), maximal tumor diameter at preoperative imaging ( > 20 mm, 1 point), and carbohydrate antigen 19-9 levels ( > 150 U/mL, 1 point). The KRAS mutation dosage exhibited comparable performance with clinical variables for survival prediction. High KRAS mutation dosages activated the cell cycle, leading to high mutation rates and poor prognosis. According to prognostic scoring systems that integrate mutation dosage with clinical factors, patients with 0 points had superior median overall survival of 97.0 months and 1-year, 3-year, and 5-year overall survival rates of 95.8%, 70.8%, and 66.4%, respectively. In contrast, patients with 3 points had worse median overall survival of only 16.0 months and 1-year, 3-year, and 5-year overall survival rates of 65.2%, 8.7%, and 8.7%, respectively. The incorporation of the KRAS G12 mutation dosage variable into prognostic scoring systems can improve clinical variable-based survival prediction, highlighting the feasibility of an integrated scoring system with clinical significance.