Although stromal fibroblasts play a critical role in cancer progression, their identities remain unclear as they exhibit high heterogeneity and plasticity. Here, a master transcription factor (mTF) ...constructing core-regulatory circuitry, PRRX1, which determines the fibroblast lineage with a myofibroblastic phenotype, is identified for the fibroblast subgroup. PRRX1 orchestrates the functional drift of fibroblasts into myofibroblastic phenotype via TGF-β signaling by remodeling a super-enhancer landscape. Such reprogrammed fibroblasts have myofibroblastic functions resulting in markedly enhanced tumorigenicity and aggressiveness of cancer. PRRX1 expression in cancer-associated fibroblast (CAF) has an unfavorable prognosis in multiple cancer types. Fibroblast-specific PRRX1 depletion induces long-term and sustained complete remission of chemotherapy-resistant cancer in genetically engineered mice models. This study reveals CAF subpopulations based on super-enhancer profiles including PRRX1. Therefore, mTFs, including PRRX1, provide another opportunity for establishing a hierarchical classification system of fibroblasts and cancer treatment by targeting fibroblasts.
Cetuximab (CTX), a monoclonal antibody against epidermal growth factor receptor, is being widely used for colorectal cancer (CRC) with wild‐type (WT) KRAS. However, its responsiveness is still very ...limited and WT KRAS is not enough to indicate such responsiveness. Here, by analyzing the gene expression data of CRC patients treated with CTX monotherapy, we have identified DUSP4, ETV5, GNB5, NT5E, and PHLDA1 as potential targets to overcome CTX resistance. We found that knockdown of any of these five genes can increase CTX sensitivity in KRAS WT cells. Interestingly, we further found that GNB5 knockdown can increase CTX sensitivity even for KRAS mutant cells. We unraveled that GNB5 overexpression contributes to CTX resistance by modulating the Akt signaling pathway from experiments and mathematical simulation. Overall, these results indicate that GNB5 might be a promising target for combination therapy with CTX irrespective of KRAS mutation.
From gene expression data analysis and mathematical modeling combined with cell experiments, we found that DUSP4, ETV5, GNB5, NT5E, and PHLDA1 expressions are correlated with cetuximab (CTX) resistance. In particular, we discovered that inhibition of GNB5 can overcome CTX resistance irrespective of KRAS mutation. We further revealed that GNB5 can regulate CTX resistance by dominantly modulating Akt signaling.
Cancer is caused by the accumulation of multiple genetic mutations, but their cooperative effects are poorly understood. Using a genome-wide analysis of all the somatic mutations in colorectal cancer ...patients in a large-scale molecular interaction network, here we find that a giant cluster of mutation-propagating modules in the network undergoes a percolation transition, a sudden critical transition from scattered small modules to a large connected cluster, during colorectal tumorigenesis. Such a large cluster ultimately results in a giant percolated cluster, which is accompanied by phenotypic changes corresponding to cancer hallmarks. Moreover, we find that the most commonly observed sequence of driver mutations in colorectal cancer has been optimized to maximize the giant percolated cluster. Our network-level percolation study shows that the cooperative effect rather than any single dominance of multiple somatic mutations is crucial in colorectal tumorigenesis.
Colorectal cancer (CRC) has been most extensively studied for characterizing genetic mutations along its development. However, we still have a poor understanding of CRC initiation due to limited ...measures of its observation and analysis. If we can unveil CRC initiation events, we might identify novel prognostic markers and therapeutic targets for early cancer detection and prevention. To tackle this problem, we establish the early CRC development model and perform transcriptome analysis of its single cell RNA-sequencing data. Interestingly, we find two subtypes, fast growing vs. slowly growing populations of distinct growth rate and gene signatures, and identify CCDC85B as a master regulator that can transform the cellular state of fast growing subtype cells into that of slowly growing subtype cells. We further validate this by
in vitro
experiments and suggest CCDC85B as a novel potential therapeutic target that may prevent malignant CRC development by suppressing stemness and uncontrolled cell proliferation.
Clinical effect of donor-derived natural killer cell infusion (DNKI) after HLA-haploidentical hematopoietic cell transplantation (HCT) was evaluated in high-risk myeloid malignancy in phase 2, ...randomized trial. Seventy-six evaluable patients (aged 21-70 years) were randomized to receive DNKI (N = 40) or not (N = 36) after haploidentical HCT. For the HCT conditioning, busulfan, fludarabine, and anti-thymocyte globulin were administered. DNKI was given twice 13 and 20 days after HCT. Four patients in the DNKI group failed to receive DNKI. In the remaining 36 patients, median DNKI doses were 1.0 × 10
/kg and 1.4 × 10
/kg on days 13 and 20, respectively. Intention-to-treat analysis showed a lower disease progression for the DNKI group (30-month cumulative incidence, 35% vs 61%, P = 0.040; subdistribution hazard ratio, 0.50). Furthermore, at 3 months after HCT, the DNKI patients showed a 1.8- and 2.6-fold higher median absolute blood count of NK and T cells, respectively. scRNA-sequencing analysis in seven study patients showed that there was a marked increase in memory-like NK cells in DNKI patients which, in turn, expanded the CD8
effector-memory T cells. In high-risk myeloid malignancy, DNKI after haploidentical HCT reduced disease progression. This enhanced graft-vs-leukemia effect may be related to the DNKI-induced, post-HCT expansion of NK and T cells. Clinical trial number: NCT02477787.
Universal infantile hepatitis B virus (HBV) vaccination may lead to an increase in vaccine escape variants, which may pose a threat to the long-term success of massive vaccination. To determine the ...prevalence of occult infections in Korean vaccinated individuals, 87 vaccinated subjects were screened for the presence of HBV DNA using both the nested PCR protocol and the VERSANT HBV DNA 3.0 assay. The mutation patterns of variants were analyzed in full-length HBV genome sequences. Their HBsAg secretion and replication capacities were investigated using both in vitro transient transfection and in vivo hydrodynamic injection. The presence of HBV DNA was confirmed in 6 subjects (6.9%). All six variants had a common mutation type (X8Del) composed of an 8-bp deletion in the C-terminal region of the HBV X gene (HBxAg). Our in vitro and in vivo analyses using the full-length HBV genome indicated that the X8Del HBxAg variant reduced the secretion of HBsAg and HBV virions compared to the wild type. In conclusion, our data suggest that a novel mutation (X8Del) may contribute to occult HBV infection in Korean vaccinated individuals via a reduced secretion of HBsAg and virions, possibly by compromising HBxAg's transacting capacity.
Apoptosis and hypertrophy of cardiomyocytes are the primary causes of heart failure and are known to be regulated by complex interactions in the underlying intracellular signaling network. Previous ...experimental studies were successful in identifying some key signaling components, but most of the findings were confined to particular experimental conditions corresponding to specific cellular contexts. A question then arises as to whether there might be essential regulatory interactions that prevail across diverse cellular contexts. To address this question, we have constructed a large-scale cardiac signaling network by integrating previous experimental results and developed a mathematical model using normalized ordinary differential equations. Specific cellular contexts were reflected to different kinetic parameters sampled from random distributions. Through extensive computer simulations with various parameter distributions, we revealed the five most essential context-independent regulatory interactions (between: (1) αAR and Gαq, (2) IP3 and calcium, (3) epac and CaMK, (4) JNK and NFAT, and (5) p38 and NFAT) for hypertrophy and apoptosis that were consistently found over all our perturbation analyses. These essential interactions are expected to be the most promising therapeutic targets across a broad spectrum of individual conditions of heart failure patients.
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the ...underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.
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•We propose a gray box framework with Boolean networks and a black box optimizer•GREY is a learned optimizer specialized in Boolean network optimization•Gray box framework can predict drug responses and reveal underlying mechanisms
The challenge of predicting cellular responses to perturbations amid the complex non-linearities of molecular interactions has spurred the development of machine learning-based models. However, interpreting these models in terms of molecular regulatory dynamics remains difficult. Conversely, logical network models like Boolean networks offer interpretability but struggle with large-scale networks due to high-dimensional search spaces. To overcome these hurdles, we introduce a scalable derivative-free optimizer, trained via meta-reinforcement learning, for Boolean network models. This approach enables prediction of anti-cancer drug responses in cancer cell lines while offering valuable insights into their molecular regulatory mechanisms.
Kim et al. present a gray box framework that combines a white box logical model with a black box optimizer, addressing challenges in interpreting molecular regulatory dynamics. The gray box framework successfully predicts anti-cancer drug responses of cancer cells, while shedding light on the underlying molecular regulatory mechanisms.
To develop a Fok-I nested polymerase chain reaction (PCR)-restriction fragment length polymorphism analysis (PRA) method for the detection of hepatitis B virus X region (HBx) V5M mutation.
Nested PCR ...was applied into DNAs from 198 chronic patients at 2 different stages 121 patients with hepatocellular carcinoma (HCC) and 77 carrier patients. To identify V5M mutants, digestion of nested PCR amplicons by the restriction enzyme Fok-I (GGA TGN9↓) was done. For size comparison, the enzyme-treated products were analyzed by electrophoresis on 2.5% agarose gels, stained with ethidium bromide, and visualized on a UV transilluminator.
The assay enabled the identification of 69 patients (sensitivity of 34.8%; 46 HCC patients and 23 carrier patients). Our data also showed that V5M prevalence in HCC patients was significantly higher than in carrier patients (47.8%, 22/46 patients vs 0%, 0/23 patients, P < 0.001), suggesting that HBxAg V5M mutation may play a pivotal role in HCC generation in chronic patients with genotype C infections.
The Fok-I nested PRA developed in this study is a reliable and cost-effective method to detect HBxAg V5M mutation in chronic patients with genotype C2 infection.
Abstract
Apoptosis and hypertrophy of cardiomyocytes are the primary causes of heart failure (HF), a global leading cause of death, and are regulated through the complicated intracellular signaling ...network, limiting the development of effective treatments due to its complexity. To identify effective therapeutic strategies for HF at a system level, we develop a large-scale comprehensive mathematical model of the cardiac signaling network by integrating all available experimental evidence. Attractor landscape analysis of the network model identifies distinct sets of control nodes that effectively suppress apoptosis and hypertrophy of cardiomyocytes under ischemic or pressure overload-induced HF, the two major types of HF. Intriguingly, our system-level analysis suggests that intervention of these control nodes may increase the efficacy of clinical drugs for HF and, of most importance, different combinations of control nodes are suggested as potentially effective candidate drug targets depending on the types of HF. Our study provides a systematic way of developing mechanism-based therapeutic strategies for HF.
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