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5 Biomarkers for Overcoming Colorectal Cancer Drug Resistance Identified
< Professor Kwang-Hyun Cho's Team > KAIST researchers have identified five biomarkers that will help them address resistance to cancer-targeting therapeutics. This new treatment strategy will bring us one step closer to precision medicine for patients who showed resistance. Colorectal cancer is one of the most common types of cancer worldwide. The number of patients has surpassed 1 million, and its five-year survival rate significantly drops to about 20 percent when metastasized. In Korea, the surge of colorectal cancer has been the highest in the last 10 years due to increasing Westernized dietary patterns and obesity. It is expected that the number and mortality rates of colorectal cancer patients will increase sharply as the nation is rapidly facing an increase in its aging population. Recently, anticancer agents targeting only specific molecules of colon cancer cells have been developed. Unlike conventional anticancer medications, these selectively treat only specific target factors, so they can significantly reduce some of the side-effects of anticancer therapy while enhancing drug efficacy. Cetuximab is the most well-known FDA approved anticancer medication. It is a biomarker that predicts drug reactivity and utilizes the presence of the ‘KRAS’ gene mutation. Cetuximab is prescribed to patients who don’t carry the KRAS gene mutation. However, even in patients without the KRAS gene mutation, the response rate of Cetuximab is only about fifty percent, and there is also resistance to drugs after targeted chemotherapy. Compared with conventional chemotherapy alone, the life expectancy only lasts five months on average. In research featured in the FEBS Journal as the cover paper for the April 7 edition, the KAIST research team led by Professor Kwang-Hyun Cho at the Department of Bio and Brain Engineering presented five additional biomarkers that could increase Cetuximab responsiveness using systems biology approach that combines genomic data analysis, mathematical modeling, and cell experiments. The experimental inhibition of newly discovered biomarkers DUSP4, ETV5, GNB5, NT5E, and PHLDA1 in colorectal cancer cells has been shown to overcome Cetuximab resistance in KRAS-normal genes. The research team confirmed that when suppressing GNB5, one of the new biomarkers, it was shown to overcome resistance to Cetuximab regardless of having a mutation in the KRAS gene. Professor Cho said, “There has not been an example of colorectal cancer treatment involving regulation of the GNB5 gene.” He continued, “Identifying the principle of drug resistance in cancer cells through systems biology and discovering new biomarkers that could be a new molecular target to overcome drug resistance suggest real potential to actualize precision medicine.” This study was supported by the National Research Foundation of Korea (NRF) and funded by the Ministry of Science and ICT (2017R1A2A1A17069642 and 2015M3A9A7067220). Image 1. The cover of FEBS Journal for April 2019
KAIST Unveils the Hidden Control Architecture of Brain Networks
(Professor Kwang-Hyun Cho and his team) A KAIST research team identified the intrinsic control architecture of brain networks. The control properties will contribute to providing a fundamental basis for the exogenous control of brain networks and, therefore, has broad implications in cognitive and clinical neuroscience. Although efficiency and robustness are often regarded as having a trade-off relationship, the human brain usually exhibits both attributes when it performs complex cognitive functions. Such optimality must be rooted in a specific coordinated control of interconnected brain regions, but the understanding of the intrinsic control architecture of brain networks is lacking. Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering and his team investigated the intrinsic control architecture of brain networks. They employed an interdisciplinary approach that spans connectomics, neuroscience, control engineering, network science, and systems biology to examine the structural brain networks of various species and compared them with the control architecture of other biological networks, as well as man-made ones, such as social, infrastructural and technological networks. In particular, the team reconstructed the structural brain networks of 100 healthy human adults by performing brain parcellation and tractography with structural and diffusion imaging data obtained from the Human Connectome Project database of the US National Institutes of Health. The team developed a framework for analyzing the control architecture of brain networks based on the minimum dominating set (MDSet), which refers to a minimal subset of nodes (MD-nodes) that control the remaining nodes with a one-step direct interaction. MD-nodes play a crucial role in various complex networks including biomolecular networks, but they have not been investigated in brain networks. By exploring and comparing the structural principles underlying the composition of MDSets of various complex networks, the team delineated their distinct control architectures. Interestingly, the team found that the proportion of MDSets in brain networks is remarkably small compared to those of other complex networks. This finding implies that brain networks may have been optimized for minimizing the cost required for controlling networks. Furthermore, the team found that the MDSets of brain networks are not solely determined by the degree of nodes, but rather strategically placed to form a particular control architecture. Consequently, the team revealed the hidden control architecture of brain networks, namely, the distributed and overlapping control architecture that is distinct from other complex networks. The team found that such a particular control architecture brings about robustness against targeted attacks (i.e., preferential attacks on high-degree nodes) which might be a fundamental basis of robust brain functions against preferential damage of high-degree nodes (i.e., brain regions). Moreover, the team found that the particular control architecture of brain networks also enables high efficiency in switching from one network state, defined by a set of node activities, to another – a capability that is crucial for traversing diverse cognitive states. Professor Cho said, “This study is the first attempt to make a quantitative comparison between brain networks and other real-world complex networks. Understanding of intrinsic control architecture underlying brain networks may enable the development of optimal interventions for therapeutic purposes or cognitive enhancement.” This research, led by Byeongwook Lee, Uiryong Kang and Hongjun Chang, was published in iScience (10.1016/j.isci.2019.02.017) on March 29, 2019. Figure 1. Schematic of identification of control architecture of brain networks. Figure 2. Identified control architectures of brain networks and other real-world complex networks.
Ultrathin Digital Camera Inspired by Xenos Peckii Eyes
(Professor Ki-Hun Jeong from the Department of Bio and Brain Engineering) The visual system of Xenos peckii, an endoparasite of paper wasps, demonstrates distinct benefits for high sensitivity and high resolution, differing from the compound eyes of most insects. Taking their unique features, a KAIST team developed an ultrathin digital camera that emulates the unique eyes of Xenos peckii. The ultrathin digital camera offers a wide field of view and high resolution in a slimmer body compared to existing imaging systems. It is expected to support various applications, such as monitoring equipment, medical imaging devices, and mobile imaging systems. Professor Ki-Hun Jeong from the Department of Bio and Brain Engineering and his team are known for mimicking biological visual organs. The team’s past research includes an LED lens based on the abdominal segments of fireflies and biologically inspired anti-reflective structures. Recently, the demand for ultrathin digital cameras has increased, due to the miniaturization of electronic and optical devices. However, most camera modules use multiple lenses along the optical axis to compensate for optical aberrations, resulting in a larger volume as well as a thicker total track length of digital cameras. Resolution and sensitivity would be compromised if these modules were to be simply reduced in size and thickness. To address this issue, the team have developed micro-optical components, inspired from the visual system of Xenos peckii, and combined them with a CMOS (complementary metal oxide semiconductor) image sensor to achieve an ultrathin digital camera. This new camera, measuring less than 2mm in thickness, emulates the eyes of Xenos peckii by using dozens of microprism arrays and microlens arrays. A microprism and microlens pair form a channel and the light-absorbing medium between the channels reduces optical crosstalk. Each channel captures the partial image at slightly different orientation, and the retrieved partial images are combined into a single image, thereby ensuring a wide field of view and high resolution. Professor Jeong said, “We have proposed a novel method of fabricating an ultrathin camera. As the first insect-inspired, ultrathin camera that integrates a microcamera on a conventional CMOS image sensor array, our study will have a significant impact in optics and related fields.” This research, led by PhD candidates Dongmin Keum and Kyung-Won Jang, was published in Light: Science & Applications on October 24, 2018. Figure 1. Natural Xenos peckii eye and the biological inspiration for the ultrathin digital camera (Light: Science & Applications 2018) Figure 2. Optical images captured by the bioinspired ultrathin digital camera (Light: Science & Applications 2018)
Skin Hardness to Estimate Better Human Thermal Status
(Professor Young-Ho Cho and Researcher Sunghyun Yoon) Under the same temperature and humidity, human thermal status may vary due to individual body constitution and climatic environment. A KAIST research team previously developed a wearable sweat rate sensor for human thermal comfort monitoring. Furthering the development, this time they proposed skin hardness as an additional, independent physiological sign to estimate human thermal status more accurately. This novel approach can be applied to developing systems incorporating human-machine interaction, which requires accurate information about human thermal status. Professor Young-Ho Cho and his team from the Department of Bio and Brain Engineering had previously studied skin temperature and sweat rate to determine human thermal comfort, and developed a watch-type sweat rate sensor that accurately and steadily detects thermal comfort last February (title: Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring ). However, skin temperature and sweat rate are still not enough to estimate exact human thermal comfort. Hence, an additional indicator is required for enhancing the accuracy and reliability of the estimation and the team selected skin hardness. When people feel hot or cold, arrector pili muscles connected to hair follicles contract and expand, and skin hardness comes from this contraction and relaxation of the muscles. Based on the phenomenon of changing skin hardness, the team proposed skin hardness as a new indicator for measuring human thermal sensation. With this new estimation model using three physiological signs for estimating human thermal status, the team conducted human experiments and verified that skin hardness is effective and independent from the two conventional physiological signs. Adding skin hardness to the conventional model can reduce errors by 23.5%, which makes its estimation more reliable. The team will develop a sensor that detects skin hardness and applies it to cognitive air-conditioning and heating systems that better interact with humans than existing systems. Professor Cho said, “Introducing this new indicator, skin hardness, elevates the reliability of measuring human thermal comfort regardless of individual body constitution and climatic environment. Based on this method, we can develop a personalized air conditioning and heating system that will allow affective interaction between humans and machines by sharing both physical and mental health conditions and emotions.” This research, led by researchers Sunghyun Yoon and Jai Kyoung Sim, was published in Scientific Reports, Vol.8, Article No.12027 on August 13, 2018. (pp.1-6) Figure 1. Measuring human thermal status through skin hardness Figure 2. The instrument used for measuring human thermal status through skin hardness
Trigger of the Hyperactivation of Fibrosis Identified
(Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering) Scientists have been investigating the negative effects that the hyperactivation of fibrosis has on fibrotic diseases and cancer. A KAIST research team unveiled a positive feedback loop that bi-stably activates fibroblasts in collaboration with Samsung Medical Center. This finding will contribute to developing therapeutic targets for both fibrosis and cancer. Human fibroblasts are dormant in normal tissue, but show radical activation during wound healing. However, the principle that induces their explosive activation has not yet been identified. Here, Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering, in collaboration with Professor Seok-Hyung Kim from Samsung Medical Center, discovered the principle of a circuit that continuously activates fibroblasts. They constructed a positive feedback loops (PFLs) where Twist1, Prrx1, and Tenascin-C (TNC) molecules consecutively activate fibroblasts. They confirmed that these are the main inducers of fibroblast activation by conducting various experiments, including molecular biological tests, mathematical modeling, animal testing, and computer simulations to conclude that they are the main inducers of fibroblast activation. According to their research, Twist 1 is a key regulator of cancer-associated fibroblasts, which directly upregulates Prrx1 and then triggers TNC, which also increases Twist1 expression. This circuit consequently forms a Twist-Prrx1-TNC positive feedback loop. Activated fibroblasts need to be deactivated after wounds are healed. However, if the PFLs continue, the fibroblasts become the major cause of worsening fibrotic diseases and cancers. Therefore, the team expects that Twist1-Prrx1-TNC positive PFLs will be applied for novel and effective therapeutic targeting of fibrotic diseases and cancers. This research was published in Nature Communications on August 1, 2018. Figure 1. Twist1 increases tenascin-c expression in cancer-associated fibroblasts. Twist1 is a potent but indirect inducer of tenascin-c (TNC), which is essential for maintaining Twist1 expression in cancer-associated fibroblasts (CAFs). Figure 2. Summary of the study. The Twist1-Prrx1-TNC positive feedback regulation provides clues for understanding the activation of fibroblasts during wound healing under normal conditions, as well as abnormally activated fibroblasts in pathological conditions such as cancerous and fibrotic diseases. Under normal conditions, the PFL acts as a reversible bistable switch by which the activation of fibroblasts is “ON" above a sufficient level of stimulation and “OFF" for the withdrawal of the stimulus. However, this switch can be permanently turned on under pathologic conditions by continued activation of the PFL, resulting in sustained proliferation of fibroblasts.
Printed Thermo-Plasmonic Heat Patterns for Neurological Disorder Treatment
(Professor Nam and Dr. Kang, right) A KAIST team presented a highly customizable neural stimulation method. The research team developed a technology that can print the heat pattern on a micron scale to enable the control of biological activities remotely. The researchers integrated a precision inkjet printing technology with bio-functional thermo-plasmonic nanoparticles to achieve a ‘selective nano-photothermal neural stimulation method.’ The research team of Professor Yoonkey Nam at the Department of Bio and Brain Engineering expects this will serve as an enabling technology for personalized precision neuromodulation therapy for patients with neurological disorders. The nano-photothermal neural stimulation method uses the thermo-plasmonic effect of metal nanoparticles to modulate the activities of neuronal networks. With the thermo-plasmonic effect, metal nanoparticles can absorb specific wavelength of illuminated light to efficiently generate localized heat. The research team discovered the inhibitory behavior of spontaneous activities of neurons upon photothermal stimulation four years ago. Since then, they have developed this technology to control hyperactive behaviors of neurons and neural circuits, which is often found in neurological disorders such as epilepsy. In order to overcome the limitation on the spatial selectivity and resolution of the previously developed nano-photothermal method, the team adopted an inkjet printing technology to micro pattern the plasmonic nanoparticles (a few tens of microns), and successfully demonstrated that the nano-photothermal stimulation can be selectively applied according to the printed patterns. The researchers applied a polyelectrolyte layer-by-layer coating method to printing substrates in a way to improve the pattern fidelity and achieve the uniform assembly of nanoparticles. The electrostatic attraction between the printed nanoparticles and the coated printing substrate also helped the stability of the attached nanoparticles. Because the polyelectrolyte coating is biocompatible, biological experiments including cell culture are possible with the technology developed in this work. Using printed gold nanorod particles in a few tens of microns resolution over a several centimeters area, the researchers showed that highly complex heat patterns can be precisely formed upon light illumination according to the printing image. Lastly, the team confirmed that the printed heat patterns can selectively and instantaneously inhibit the activities of cultured hippocampal neurons upon near-infrared light illumination. Because the printing process is applicable to thin and flexible substrates, the technology can be easily applied to implantable neurological disorder treatment devices and wearable devices. By selectively applying the heat patterns to only the desired cellular areas, customized and personalized photothermal neuromodulation therapy can be applied to patients. “The fact that any desired heat patterns can be simply ‘printed’ anywhere broadens the applicability of this technology in many engineering fields. In bioengineering, it can be applied to neural interfaces using light and heat to modulate physiological functions. As another engineering application, for example, printed heat patterns can be used as a new concept of anti-counterfeit applications,” said the principal investigator, Yoonkey Nam at KAIST. This work, led mainly by Dr. Hongki Kang, was published in ACS Nano on February 5th 2018.
Finding Human Thermal Comfort with a Watch-type Sweat Rate Sensor
(from left: Professor Young-Ho Cho and Researcher SungHyun Yoon) KAIST developed a watch-type sweat rate sensor. This subminiature device can detect human thermal comfort accurately and steadily by measuring an individual’s sweat rate. It is natural to sweat more in the summer and less in the winter; however, an individual’s sweat rate may vary in a given environment. Therefore, sweat can be an excellent proxy for sensing core body temperature. Conventional sweat rate sensors using natural ventilation require bulky external devices, such as pumps and ice condensers. They are usually for physiological experiments, hence they need a manual ventilation process or high power, bulky thermos-pneumatic actuators to lift sweat rate detection chambers above skin for continuous measurement. There is also a small sweat rate sensor, but it needs a long recovery period. To overcome these problems, Professor Young-Ho Cho and his team from the Department of Bio and Brain Engineering developed a lightweight, watch-type sweat sensor. The team integrated miniaturized thermos-pneumatic actuators for automatic natural ventilation, which allows sweat to be measured continuously. This watch-type sensor measures sweat rate with the humidity rising rate when the chamber is closed during skin contact. Since the team integrated thermos-pneumatic actuators, the chamber no longer needs to be separated manually from skin after each measurement in order for the chamber to ventilate the collected humidity. Moreover, this sensor is wind-resistant enough to be used for portable and wearable devices. The team identified that the sensor operates steadily with air velocity ranging up to 1.5m/s, equivalent to the average human walking speed. Although this subminiature sensor (35mm x 25mm) only weighs 30 grams, it operates continuously for more than four hours using the conventional wrist watch batteries. The team plans to utilize this technology for developing a new concept of cognitive air-conditioning systems recognizing Human thermal status directly; while the conventional air-conditioning systems measuring air temperature and humidity. Professor Cho said, “Our sensor for human thermal comfort monitoring can be applied to customized or smart air conditioners. Furthermore, there will be more demands for both physical and mental healthcare, hence this technology will serve as a new platform for personalized emotional communion between humans and devices.” This research, led by researchers Jai Kyoung Sim and SungHyun Yoon, was published in Scientific Reports on January 19, 2018. Figure1. The fabricated watch-type sweat rate sensor for human thermal comfort monitoring Figure 2. Views of the watch-type sweat rate sensor Figure 3. Operation of the watch-type sweat rate sensor
Technology to Find Optimum Drug Target for Cancer Developed
(Professor Kwang-Hyun Cho (right) and lead author Dr. Minsoo Choi) A KAIST research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering developed technology to find the optimum drug target according to the type of cancer cell. The team used systems biology to analyze molecular network dynamics that reflect genetic mutations in cancer cells and to predict drug response. The technology could contribute greatly to future anti-cancer drug development. There are many types of genetic variations found in cancer cells, including gene mutations and copy number variations. These variations differ in cancer cells even within the same type of cancer, and thus the drug response varies cell by cell. Cancer researchers worked towards identifying frequently occurring genetic variations in cancer patients and, in particular, the mutations that can be used as an index for specific drugs. Previous studies focused on identifying a single genetic mutation or creating an analysis of the structural characteristics of a gene network. However, this approach was limited in its inability to explain the biological properties of cancer which are induced by various gene and protein interactions in cancer cells, which result in differences in drug response. Gene mutations in cancer cells not only affect the function of the affected gene, but also other genes that interact with the mutated gene and proteins. As a consequence, one mutation could lead to changes in the dynamical properties of the molecular network. Therefore, the responses to anti-cancer drugs by cancer cells differ. The current treatment approach that ignores molecular network dynamics and targets a few cancer-related genes is only effective on a fraction of patients, while many other patients exhibit resistance to the drug. Professor Cho’s team integrated a large-scale computer simulation using super-computing and cellular experiments to analyze changes in molecular network dynamics in cancer cells. This led to development of technology to find the optimum drug target according to the type of cancer cells by predicting drug response. This technology was applied to the molecular network of known tumor suppressor p53. The team used large-scale cancer cell genomic data available from The Cancer Cell Line Encyclopedia (CCLE) to construct different molecular networks specific to the characteristics of genetic variations. Perturbation analysis on drug response in each molecular network was used to quantify changes in cancer cells from drug response and similar networks were clustered. Then, computer simulations were used to analyze the synergetic effects in terms of efficacy and combination to predict the level of drug response. Based on the simulation results from various cancer cell lines including lung, breast, bone, skin, kidney, and ovary cancers were used in drug response experiments for compare analysis. This technique can be applied in any molecular network to identify the optimum drug target for personalized medicine. The research team suggests that the technology can analyze varying drug response due to the heterogeneity of cancer cells by considering the overall modulatory interactions rather than focusing only on a specific gene or protein. Further, the technology aids the prediction of causes of drug resistance and thus the identification of the optimum drug target to inhibit the resistance. This could be core source technology that can be used in drug repositioning, a process of applying existing drugs to new disease targets. Professor Cho said, “Genetic variations in cancer cells are the cause of diverse drug response, but a complete analysis had not yet been made.” He continued, “Systems biology allowed the simulation of drug responses by cancer cell molecular networks to identify fundamental principles of drug response and optimum drug targets using a new conceptual approach.” This research was published in Nature Communications on December 5 and was funded by Ministry of Science and ICT and National Research Foundation of Korea. (Figure 1. Drug response prediction for each cancer cell type from computer simulation and cellular experiment verification for comparison) (Figure 2. Drug response prediction based on cancer cell molecular network dynamics and clustering of cancer cells by their molecular networks) (Figure 3. Identification of drug target for each cancer cell type by cellular molecular network analysis and establishment for personalized medicine strategy for each cancer patient)
Mutant Gene Network in Colon Cancer Identified
The principles of the gene network for colon tumorigenesis have been identified by a KAIST research team. The principles will be used to find the molecular target for effective anti-cancer drugs in the future. Further, this research gained attention for using a systems biology approach, which is an integrated research area of IT and BT. The KAIST research team led by Professor Kwang-Hyun Cho for the Department of Bio and Brain Engineering succeeded in the identification. Conducted by Dr. Dongkwan Shin and student researchers Jonghoon Lee and Jeong-Ryeol Gong, the research was published in Nature Communications online on November 2. Human cancer is caused by genetic mutations. The frequency of the mutations differs by the type of cancer; for example, only around 10 mutations are found in leukemia and childhood cancer, but an average of 50 mutations are found in adult solid cancers and even hundreds of mutations are found in cancers due to external factors, such as with lung cancer. Cancer researchers around the world are working to identify frequently found genetic mutations in patients, and in turn identify important cancer-inducing genes (called ‘driver genes’) to develop targets for anti-cancer drugs. However, gene mutations not only affect their own functions but also affect other genes through interactions. Therefore, there are limitations in current treatments targeting a few cancer-inducing genes without further knowledge on gene networks, hence current drugs are only effective in a few patients and often induce drug resistance. Professor Cho’s team used large-scale genomic data from cancer patients to construct a mathematical model on the cooperative effects of multiple genetic mutations found in gene interaction networks. The basis of the model construction was The Cancer Genome Atlas (TCGA) presented at the International Cancer Genome Consortium. The team successfully quantified the effects of mutations in gene networks to group colon cancer patients by clinical characteristics. Further, the critical transition phenomenon that occurs in tumorigenesis was identified using large-scale computer simulation analysis, which was the first hidden gene network principle to be identified. Critical transition is the phenomenon in which the state of matter is suddenly changed through phase transition. It was not possible to identify the presence of transition phenomenon in the past, as it was difficult to track the sequence of gene mutations during tumorigenesis. The research team used a systems biology-based research method to find that colon cancer tumorigenesis shows a critical transition phenomenon if the known driver gene mutations follow sequentially. Using the developed mathematical model, it can be possible to develop a new anti-cancer targeting drug that most effectively inhibits the effects of many gene mutations found in cancer patients. In particular, not only driver genes, but also other passenger genes affected by the gene mutations, could be evaluated to find the most effective drug targets. Professor Cho said, “Little was known about the contribution of many gene mutations during tumorigenesis.” He continued, “In this research, a systems biology approach identified the principle of gene networks for the first time to suggest the possibility of anti-cancer drug target identification from a new perspective.” This research was funded by the Ministry of Science and ICT and the National Research Foundation of Korea. Figure1. Formation of giant clusters via mutation propagation Figure2. Critical transition phenomenon by cooperative effect of mutations in tumorigenesis
Discovery of an Optimal Drug Combination: Overcoming Resistance to Targeted Drugs for Liver Cancer
A KAIST research team presented a novel method for improving medication treatment for liver cancer using Systems Biology, combining research from information technology and the life sciences. Professor Kwang-Hyun Cho in the Department of Bio and Brain Engineering at KAIST conducted the research in collaboration with Professor Jung-Hwan Yoon in the Department of Internal Medicine at Seoul National University Hospital. This research was published in Hepatology in September 2017 (available online from August 24, 2017). Liver cancer is the fifth and seventh most common cancer found in men and women throughout the world, which places it second in the cause of cancer deaths. In particular, Korea has 28.4 deaths from liver cancer per 100,000 persons, the highest death rate among OECD countries and twice that of Japan. Each year in Korea, 16,000 people get liver cancer on average, yet the five-year survival rate stands below 12%. According to the National Cancer Information Center, lung cancer (17,399) took the highest portion of cancer-related deaths, followed by liver cancer (11,311) based on last year data. Liver cancer is known to carry the highest social cost in comparison to other cancers and it causes the highest fatality in earlier age groups (40s-50s). In that sense, it is necessary to develop a new treatment that mitigates side effects yet elevates the survival rate. There are ways in which liver cancer can be cured, such as surgery, embolization, and medication treatments; however, the options become limited for curing progressive cancer, a stage in which surgical methods cannot be executed. Among anticancer medications, Sorafenib, a drug known for enhancing the survival rate of cancer patients, is a unique drug allowed for use as a targeted anticancer medication for progressive liver cancer patients. Its sales reached more than ten billion KRW annually in Korea, but its efficacy works on only about 20% of the treated patients. Also, acquired resistance to Sorafenib is emerging. Additionally, the action mechanism and resistance mechanism of Sorafenib is only vaguely identified.Although Sorafenib only extends the survival rate of terminal cancer patients less than three months on average, it is widely being used because drugs developed by global pharmaceutical companies failed to outperform its effectiveness. Professor Cho’s research team analyzed the expression changes of genes in cell lines in response to Sorafenib in order to identify the effect and the resistance mechanism of Sorafenib. As a result, the team discovered the resistance mechanism of Sorafenib using Systems Biology analysis. By combining computer simulations and biological experiments, it was revealed that protein disulfide isomerase (PDI) plays a crucial role in the resistance mechanism of Sorafenib and that its efficacy can be improved significantly by blocking PDI. The research team used mice in the experiment and discovered the synergic effect of PDI inhibition with Sorafenib for reducing liver cancer cells, known as hepatocellular carcinoma. Also, more PDIs are shown in tissue from patients who possess a resistance to Sorafenib. From these findings, the team could identify the possibility of its clinical applications. The team also confirmed these findings from clinical data through a retrospective cohort study. “Molecules that play an important role in cell lines are mostly put under complex regulation. For this reason, the existing biological research has a fundamental limitations for discovering its underlying principles,” Professor Cho said. “This research is a representative case of overcoming this limitation of traditional life science research by using a Systems Biology approach, combining IT and life science. It suggests the possibility of developing a new method that overcomes drug resistance with a network analysis of the targeted drug action mechanism of cancer.” The research was supported by the National Research Foundation of Korea (NRF) and funded by the Ministry of Science and ICT. (Figure 1. Simulation results from cellular experiments using hepatocellular carcinoma) (Figure 2. Network analysis and computer simulation by using the endoplasmic reticulum (ER) stress network) (Figure 3. ER stress network model)
Gout Diagnostic Strip Using a Single Teardrop
A novel diagnostic strip for gout patients using a single teardrop has been announced by KAIST research team. This technology analyzes biological molecules in tears for a non-invasive diagnosis, significantly reducing the time and expense previously required for a diagnosis. The research team under Professor Ki-Hun Jeong of the Department of Bio and Brain Engineering succeeded in developing an affordable and elaborate gout diagnostic strip by depositing metal nanoparticles on paper. This technology can not only be used in diagnostic medicine and drug testing, but also in various other areas such as field diagnoses that require prompt and accurate detection of a certain substance. Gout induces pain in joints due to needle-shaped uric acid crystal build up. In general, therapeutic treatments exist to administer pain relief, stimulate uric acid discharge, and uric acid depressant. Such treatments work for temporary relief, but there have significant limitations. Thus, patients are required to regularly check uric acid concentrations, as well as control their diets. Therefore, simpler ways to measure uric acid would greatly benefit gout control and its prevention in a more affordable and convenient manner. Existing gout diagnostic techniques include measuring uric acid concentrations from blood samples or observing uric acid crystals from joint synovial fluid under a microscope. These existing methods are invasive and time consuming. To overcome their limitations, the research team uniformly deposited gold nanoislands with nanoplasnomics properties on the surface of paper that can easily collect tears. Nanoplasnomics techniques collect light on the surface of a metal nanostructure, and can be applied to disease and health diagnostic indicators as well as for genetic material detection. Further, metals such as gold absorb stronger light when it is irradiated, and thus can maximize light concentration on board surfaces while maintaining the properties of paper. The developed metal nanostructure production technology allows the flexible manufacturing of nanostructures on a large surface, which in turn allows flexible control of light concentrations. The research team grafted surface-enhanced Raman spectroscopy on paper diagnostic strips to allow uric acid concentration measurements in teardrops without additional indicators. The measured concentration in teardrops can be compared to blood uric acid concentrations for diagnosing gout. Professor Jeong explained, “Based on these research results, our strip will make it possible to conduct low-cost, no indicator, supersensitive biological molecule analysis and fast field diagnosis using tears.” He continued, “Tears, as well as various other bodily fluids, can be used to contribute to disease diagnosis and physiological functional research.” Ph.D. candidate Moonseong Park participated in the research as the first author of the paper that was published in the online edition of ACS Nano on December 14, 2016. Park said, “The strip will allow fast and simple field diagnosis, and can be produced on a large scale using the existing semiconductor process.” (Figure 1. Optical image of paper gout diagnostic strip covered with gold) (Figure 2. Scanning delectron microscopic image of paper gout diagnostic strip) (Figure 3. Scanning electron microscope image of cellulos fiber coated with gold nanoislands) (Figure 4. Gout diagnosis using tears)
Brain Cognitive Engineering Experts from Korea and Abroad Gather at KAIST
The symposium presents recent and future research trends in brain and cognitive engineering. KAIST hosted the Brain Cognitive Engineering Symposium on September 24, 2015, at the Dream Hall of the Chung Moon Soul building on campus. Around 100 experts in the field of neuroscience participated. Organized by the Department of Bio and Brain Engineering at KAIST, the symposium celebrated the establishment of the Brain Cognitive Engineering Program at the university and examined the recent research trends in neuroscience. Six neuroscience experts presented their research and held discussions. Professor Paul M. Thompson of the University of Southern California (USC), a renowned scientist in neurology imaging genetics, gave a speech entitled “The ENIGMA Project: Mapping Disease and Genetic Effects on the Human Brain in 30,000 People Worldwide.” Professor Jae-seung Jeong of KAIST’s Department of Bio and Brain Engineering, Director Sung-Gi Kim of IBS Center for Neuroscience Imaging Research, Professor Sung-Hwan Lee of Korea University’s Department of Brain Engineering, Professor Cheil-Moon of DGIST’s Department of Brain and Cognitive Science, and Professor Jun-Tani of KAIST’s Department of Electrical Engineering also participated in the symposium. Participants discussed the most recent findings in the field of brain science such as the education and research trends of brain cognitive engineering, trends of the world’s brain integrated science, the prospects of brain cognitive engineering program, brain activities that induce blood flow and fMRI, activity production in the brain cortex model as well as the development of functional hierarchy for the motor visual perception, and the neurorobotics research. Professor Jeong said that “this symposium is a place for examination of the most recent research findings in the field of neuroscience as well as for discussion of its education,”and that “it would be an important opportunity for learning research on brain’s basic mechanisms as well as its applications.”
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