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KAIST Awakens dormant immune cells inside tumors to attack cancer
<(From Left) Professor Ji-Ho Park, Dr. Jun-Hee Han from the Department of Bio and Brain Engineering>
Within tumors in the human body, there are immune cells (macrophages) capable of fighting cancer, but they have been unable to perform their roles properly due to suppression by the tumor. KAIST researchers have overcome this limitation by developing a new therapeutic approach that directly converts immune cells inside tumors into anticancer cell therapies.
KAIST (President Kwang Hyung Lee) announced on the 30th that a research team led by Professor Ji-Ho Park of the Department of Bio and Brain Engineering has developed a therapy in which, when a drug is injected directly into a tumor, macrophages already present in the body absorb it, produce CAR (a cancer-recognizing device) proteins on their own, and are converted into anticancer immune cells known as “CAR-macrophages.”
Solid tumors—such as gastric, lung, and liver cancers—grow as dense masses, making it difficult for immune cells to infiltrate tumors or maintain their function. As a result, the effectiveness of existing immune cell therapies has been limited.
CAR-macrophages, which have recently attracted attention as a next-generation immunotherapy, have the advantage of directly engulfing cancer cells while simultaneously activating surrounding immune cells to amplify anticancer responses.
However, conventional CAR-macrophage therapies require immune cells to be extracted from a patient’s blood, followed by cell culture and genetic modification. This process is time-consuming, costly, and has limited feasibility for real-world patient applications.
To address this challenge, the research team focused on “tumor-associated macrophages” that are already accumulated around tumors.
They developed a strategy to directly reprogram immune cells in the body by loading lipid nanoparticles—designed to be readily absorbed by macrophages—with both mRNA encoding cancer-recognition information and an immunostimulant that activates immune responses.
In other words, in this study, CAR-macrophages were created by “directly converting the body’s own macrophages into anticancer cell therapies inside the body.”
<Figure . Schematic illustration of the strategy for in vivo CAR-macrophage generation and cancer cell eradication via co-delivery of CAR mRNA and immunostimulants using lipid nanoparticles (LNPs)>
When this therapeutic agent was injected into tumors, macrophages rapidly absorbed it and began producing proteins that recognize cancer cells, while immune signaling was simultaneously activated. As a result, the generated “enhanced CAR-macrophages” showed markedly improved cancer cell–killing ability and activated surrounding immune cells, producing a powerful anticancer effect.
In animal models of melanoma (the most dangerous form of skin cancer), tumor growth was significantly suppressed, and the therapeutic effect was shown to have the potential to extend beyond the local tumor site to induce systemic immune responses.
Professor Ji-Ho Park stated, “This study presents a new concept of immune cell therapy that generates anticancer immune cells directly inside the patient’s body,” adding that “it is particularly meaningful in that it simultaneously overcomes the key limitations of existing CAR-macrophage therapies—delivery efficiency and the immunosuppressive tumor environment.”
This research was led by Jun-Hee Han, Ph.D., of the Department of Bio and Brain Engineering at KAIST as the first author, and the results were published on November 18 in ACS Nano, an international journal in the field of nanotechnology.
※ Paper title: “In Situ Chimeric Antigen Receptor Macrophage Therapy via Co-Delivery of mRNA and Immunostimulant,” Authors: Jun-Hee Han (first author), Erinn Fagan, Kyunghwan Yeom, Ji-Ho Park (corresponding author), DOI: 10.1021/acsnano.5c09138
This research was supported by the Mid-Career Researcher Program of the National Research Foundation of Korea.
2025.12.31 View 4423 -
Octopus-Inspired 3D Micro-LEDs Pave the Way for Selective Pancreatic Cancer Therapy
<(From Left) Professor Keon Jae Lee, Professor Tae-Hyuk Kwon, Ph.D candidate Min Seo Kim, Dr. Jae Hee Lee, Dr. Chae Gyu Lee>
-KAIST and UNIST Researchers Develop Shape-Morphing Device to Overcome Pancreatic Tumor Microenvironment Barriers
Conventional pancreatic cancer treatments face a critical hurdle due to the dense tumor microenvironment (TME). This biological barrier surrounds the tumor, severely limiting the infiltration of chemotherapy agents and immune cells. While photodynamic therapy (PDT) offers a promising alternative, existing external light sources, such as lasers, fail to penetrate deep tissues effectively and pose risks of thermal damage and inflammation to healthy organs
To address these challenges, Professor Keon Jae Lee’s team at KAIST, in collaboration with Professor Tae-Hyuk Kwon at UNIST, developed an implantable, shape-morphing 3D micro-LED device capable of effectively delivering light to deep tissues. The key technology lies in the device’s flexible, octopus-like architecture, which allows it to wrap around the entire pancreatic tumor. This mechanical compliance ensures uniform light delivery to the tumor despite the tumor’s physiological expansion or contraction, enabling continuous, low intensity photostimulation that precisely targets cancer cells while preserving normal tissue.
In in-vivo experiments involving mouse models, the device demonstrated remarkable therapeutic efficacy. Within just three days, tumor fibrous tissue was reduced by 64%, and the pancreatic tissue successfully reverted to normal tissue, overcoming the limitations of conventional PDT.
Prof. Keon Jae Lee said, "This research presents a new therapeutic paradigm by directly disrupting the tumor microenvironment, the primary obstacle in pancreatic cancer treatment." He added, "We aim to expand this technology into a smart platform integrated with artificial intelligence (AI) for real-time tumor monitoring and personalized treatment. We are currently seeking partners to advance clinical trials and commercialization for human application."
<Overall concept of 3D Shape-morphing micro-LEDs (SMLEDs). The 3D long-term, low-intensity photodynamic therapy (PDT) system attaches to the pancreatic surface, ensuring stable and continuous light delivery. Initially maintaining a 2D structure, the system morphs into a 3D structure upon implantation to conform to the shape of the pancreas. In in vivo experiments, the device maintained stable adhesion without detachment for four weeks and reduced the pancreatic tumor size by 64%.>
Professor Tae-Hyuk Kwon commented, "While phototherapy is effective for selective cancer treatment, conventional technologies have been limited by the challenges of delivering light to deep tissues and developing suitable photosensitizers." He added, "Building on this breakthrough, we aim to expand effective immune-based therapeutic strategies for targeting intractable cancers."
<Cover Image. The 3D long-term, low-intensity photodynamic therapy (PDT) system, developed by Professor Keon Jae Lee's team at the Department of Materials Science and Engineering at KAIST, was featured as the cover article of the international journal Advanced Materials>
The result, titled "Deeply Implantable, Shape-Morphing, 3D MicroLEDs for Pancreatic Cancer Therapy," was featured as the cover article in Advanced Materials (Volume 37) on December 10, 2025.
2025.12.11 View 2996 -
KAIST Identifies Master Regulator Blocking Immunotherapy, Paving the Way for a New Lung Cancer Treatment
Immune checkpoint inhibitors, a class of immunotherapies that help immune cells attack cancer more effectively, have revolutionized cancer treatment. However, fewer than 20% of patients respond to these treatments, highlighting the urgent need for new strategies tailored to both responders and non-responders.
KAIST researchers have discovered that 'DEAD-box helicases 54 (DDX54)', a type of RNA-binding protein, is the master regulator that hinders the effectiveness of immunotherapy—opening a new path for lung cancer treatment. This breakthrough technology has been transferred to faculty startup BioRevert Inc., where it is currently being developed as a companion therapeutic and is expected to enter clinical trials by 2028.
< Photo 1. (From left) Researcher Jungeun Lee, Professor Kwang-Hyun Cho and Postdoctoral Researcher Jeong-Ryeol Gong of the Department of Bio and Brain Engineering at KAIST >
KAIST (represented by President Kwang-Hyung Lee) announced on April 8 that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering had identified DDX54 as a critical factor that determines the immune evasion capacity of lung cancer cells. They demonstrated that suppressing DDX54 enhances immune cell infiltration into tumors and significantly improves the efficacy of immunotherapy.
Immunotherapy using anti-PD-1 or anti-PD-L1 antibodies is considered a powerful approach in cancer treatment. However, its low response rate limits the number of patients who actually benefit.
To identify likely responders, tumor mutational burden (TMB) has recently been approved by the FDA as a key biomarker for immunotherapy. Cancers with high mutation rates are thought to be more responsive to immune checkpoint inhibitors. However, even tumors with high TMB can display an “immune-desert” phenotype—where immune cell infiltration is severely limited—resulting in poor treatment responses.
< Figure 1. DDX54 was identified as the master regulator that induces resistance to immunotherapy by orchestrating suppression of immune cell infiltration through cancer tissues as lung cancer cells become immune-evasive >
Professor Kwang-Hyun Cho's research team compared transcriptome and genome data of lung cancer patients with immune evasion capabilities through gene regulatory network analysis (A) and discovered DDX54, a master regulator that induces resistance to immunotherapy (B-F).
This study is especially significant in that it successfully demonstrated that suppressing DDX54 in immune-desert lung tumors can overcome immunotherapy resistance and improve treatment outcomes.
The team used transcriptomic and genomic data from immune-evasive lung cancer patients and employed systems biology techniques to infer gene regulatory networks. Through this analysis, they identified DDX54 as a central regulator in the immune evasion of lung cancer cells.
In a syngeneic mouse model, the suppression of DDX54 led to significant increases in the infiltration of anti-cancer immune cells such as T cells and NK cells, and greatly improved the response to immunotherapy.
Single-cell transcriptomic and spatial transcriptomic analyses further showed that combination therapy targeting DDX54 promoted the differentiation of T cells and memory T cells that suppress tumors, while reducing the infiltration of regulatory T cells and exhausted T cells that support tumor growth.
< Figure 2. In the syngeneic mouse model made of lung cancer cells, it was confirmed that inhibiting DDX54 reversed the immune-evasion ability of cancer cells and enhanced the sensitivity to anti-PD-1 therapy >
In a syngeneic mouse model made of lung cancer cells exhibiting immunotherapy resistance, the treatment applied after DDX54 inhibition resulted in statistically significant inhibition of lung cancer growth (B-D) and a significant increase in immune cell infiltration into the tumor tissue (E, F).
The mechanism is believed to involve DDX54 suppression inactivating signaling pathways such as JAK-STAT, MYC, and NF-κB, thereby downregulating immune-evasive proteins CD38 and CD47. This also reduced the infiltration of circulating monocytes—which promote tumor development—and promoted the differentiation of M1 macrophages that play anti-tumor roles.
Professor Kwang-Hyun Cho stated, “We have, for the first time, identified a master regulatory factor that enables immune evasion in lung cancer cells. By targeting this factor, we developed a new therapeutic strategy that can induce responsiveness to immunotherapy in previously resistant cancers.”
He added, “The discovery of DDX54—hidden within the complex molecular networks of cancer cells—was made possible through the systematic integration of systems biology, combining IT and BT.”
The study, led by Professor Kwang-Hyun Cho, was published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 2, 2025, with Jeong-Ryeol Gong being the first author, Jungeun Lee, a co-first author, and Younghyun Han, a co-author of the article.
< Figure 3. Single-cell transcriptome and spatial transcriptome analysis confirmed that knockdown of DDX54 increased immune cell infiltration into cancer tissues >
In a syngeneic mouse model made of lung cancer cells that underwent immunotherapy in combination with DDX54 inhibition, single-cell transcriptome (H-L) and spatial transcriptome (A-G) analysis of immune cells infiltrating inside cancer tissues were performed. As a result, it was confirmed that anticancer immune cells such as T cells, B cells, and NK cells actively infiltrated the core of lung cancer tissues when DDX54 inhibition and immunotherapy were concurrently administered.
(Paper title: “DDX54 downregulation enhances anti-PD1 therapy in immune-desert lung tumors with high tumor mutational burden,” DOI: https://doi.org/10.1073/pnas.2412310122)
This work was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Mid-Career Research Program and Basic Research Laboratory Program.
< Figure 4. The identified master regulator DDX54 was confirmed to induce CD38 and CD47 expression through Jak-Stat3, MYC, and NF-κB activation. >
DDX54 activates the Jak-Stat3, MYC, and NF-κB pathways in lung cancer cells to increase CD38 and CD47 expression (A-G). This creates a cancer microenvironment that contributes to cancer development (H) and ultimately induces immune anticancer treatment resistance.
< Figure 5. It was confirmed that an immune-inflamed environment can be created by combining DDX54 inhibition and immune checkpoint inhibitor (ICI) therapy. >
When DDX54 inhibition and ICI therapy are simultaneously administered, the cancer cell characteristics change, the immune evasion ability is restored, and the environment is transformed into an ‘immune-activated’ environment in which immune cells easily infiltrate cancer tissues. This strengthens the anticancer immune response, thereby increasing the sensitivity of immunotherapy even in lung cancer tissues that previously had low responsiveness to immunotherapy.
2025.04.08 View 15450 -
KAIST Uncovers the Principles of Gene Expression Regulation in Cancer and Cellular Functions
< (From left) Professor Seyun Kim, Professor Gwangrog Lee, Dr. Hyoungjoon Ahn, Dr. Jeongmin Yu, Professor Won-Ki Cho, and (below) PhD candidate Kwangmin Ryu of the Department of Biological Sciences>
A research team at KAIST has identified the core gene expression networks regulated by key proteins that fundamentally drive phenomena such as cancer development, metastasis, tissue differentiation from stem cells, and neural activation processes. This discovery lays the foundation for developing innovative therapeutic technologies.
On the 22nd of January, KAIST (represented by President Kwang Hyung Lee) announced that the joint research team led by Professors Seyun Kim, Gwangrog Lee, and Won-Ki Cho from the Department of Biological Sciences had uncovered essential mechanisms controlling gene expression in animal cells.
Inositol phosphate metabolites produced by inositol metabolism enzymes serve as vital secondary messengers in eukaryotic cell signaling systems and are broadly implicated in cancer, obesity, diabetes, and neurological disorders.
The research team demonstrated that the inositol polyphosphate multikinase (IPMK) enzyme, a key player in the inositol metabolism system, acts as a critical transcriptional activator within the core gene expression networks of animal cells. Notably, although IPMK was previously reported to play an important role in the transcription process governed by serum response factor (SRF), a representative transcription factor in animal cells, the precise mechanism of its action was unclear.
SRF is a transcription factor directly controlling the expression of at least 200–300 genes, regulating cell growth, proliferation, apoptosis, and motility, and is indispensable for organ development, such as in the heart.
The team discovered that IPMK binds directly to SRF, altering the three-dimensional structure of the SRF protein. This interaction facilitates the transcriptional activity of various genes through the SRF activated by IPMK, demonstrating that IPMK acts as a critical regulatory switch to enhance SRF's protein activity.
< Figure 1. The serum response factor (SRF) protein, a key transcription factor in animal cells, directly binds to inositol polyphosphate multikinase (IPMK) enzyme and undergoes structural change to acquire DNA binding ability, and precisely regulates growth and differentiation of animal cells through transcriptional activation. >
The team further verified that disruptions in the direct interaction between IPMK and SRF lead to the reduced functionality and activity of SRF, causing severe impairments in gene expression.
By highlighting the significance of the intrinsically disordered region (IDR) in SRF, the researchers underscored the biological importance of intrinsically disordered proteins (IDPs). Unlike most proteins that adopt distinct structures through folding, IDPs, including those with IDRs, do not exhibit specific structures but play crucial biological roles, attracting significant attention in the scientific community.
Professor Seyun Kim commented, "This study provides a vital mechanism proving that IPMK, a key enzyme in the inositol metabolism system, is a major transcriptional activator in the core gene expression network of animal cells. By understanding fundamental processes such as cancer development and metastasis, tissue differentiation from stem cells, and neural activation through SRF, we hope this discovery will lead to the broad application of innovative therapeutic technologies."
The findings were published on January 7th in the international journal Nucleic Acids Research (IF=16.7, top 1.8% in Biochemistry and Molecular Biology), under the title “Single-molecule analysis reveals that IPMK enhances the DNA-binding activity of the transcription factor SRF" (DOI: 10.1093/nar/gkae1281).
This research was supported by the National Research Foundation of Korea's Mid-career Research Program, Leading Research Center Program, and Global Research Laboratory Program, as well as by the Suh Kyungbae Science Foundation and the Samsung Future Technology Development Program.
2025.01.24 View 19683 -
KAIST Develops Foundational Technology to Revert Cancer Cells to Normal Cells
Despite the development of numerous cancer treatment technologies, the common goal of current cancer therapies is to eliminate cancer cells. This approach, however, faces fundamental limitations, including cancer cells developing resistance and returning, as well as severe side effects from the destruction of healthy cells.
< (From top left) Bio and Brain Engineering PhD candidates Juhee Kim, Jeong-Ryeol Gong, Chun-Kyung Lee, and Hoon-Min Kim posed for a group photo with Professor Kwang-Hyun Cho >
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of December that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering has developed a groundbreaking technology that can treat colon cancer by converting cancer cells into a state resembling normal colon cells without killing them, thus avoiding side effects.
The research team focused on the observation that during the oncogenesis process, normal cells regress along their differentiation trajectory. Building on this insight, they developed a technology to create a digital twin of the gene network associated with the differentiation trajectory of normal cells.
< Figure 1. Technology for creating a digital twin of a gene network from single-cell transcriptome data of a normal cell differentiation trajectory. Professor Kwang-Hyun Cho's research team developed a digital twin creation technology that precisely observes the dynamics of gene regulatory relationships during the process of normal cells differentiating along a differentiation trajectory and analyzes the relationships among key genes to build a mathematical model that can be simulated (A-F). In addition, they developed a technology to discover key regulatory factors that control the differentiation trajectory of normal cells by simulating and analyzing this digital twin. >
< Figure 2. Digital twin simulation simulating the differentiation trajectory of normal colon cells. The dynamics of single-cell transcriptome data for the differentiation trajectory of normal colon cells were analyzed (A) and a digital twin of the gene network was developed representing the regulatory relationships of key genes in this differentiation trajectory (B). The simulation results of the digital twin confirm that it readily reproduces the dynamics of single-cell transcriptome data (C, D). >
Through simulation analysis, the team systematically identified master molecular switches that induce normal cell differentiation. When these switches were applied to colon cancer cells, the cancer cells reverted to a normal-like state, a result confirmed through molecular and cellular experiments as well as animal studies.
< Figure 3. Discovery of top-level key control factors that induce differentiation of normal colon cells. By applying control factor discovery technology to the digital twin model, three genes, HDAC2, FOXA2, and MYB, were discovered as key control factors that induce differentiation of normal colon cells (A, B). The results of simulation analysis of the regulatory effects of the discovered control factors through the digital twin confirmed that they could induce complete differentiation of colon cells (C). >
< Figure 4. Verification of the effect of the key control factors discovered using colon cancer cells and animal experiments on the reversibility of colon cancer. The key control factors of the normal colon cell differentiation trajectory discovered through digital twin simulation analysis were applied to actual colon cancer cells and colon cancer mouse animal models to experimentally verify the effect of cancer reversibility. The key control factors significantly reduced the proliferation of three colon cancer cell lines (A), and this was confirmed in the same way in animal models (B-D). >
This research demonstrates that cancer cell reversion can be systematically achieved by analyzing and utilizing the digital twin of the cancer cell gene network, rather than relying on serendipitous discoveries. The findings hold significant promise for developing reversible cancer therapies that can be applied to various types of cancer.
< Figure 5. The change in overall gene expression was confirmed through the regulation of the identified key regulatory factors, which converted the state of colon cancer cells to that of normal colon cells. The transcriptomes of colon cancer tissues and normal colon tissues from more than 400 colon cancer patients were compared with the transcriptomes of colon cancer cell lines and reversible colon cancer cell lines, respectively. The comparison results confirmed that the regulation of the identified key regulatory factors converted all three colon cancer cell lines to a state similar to the transcriptome expression of normal colon tissues. >
Professor Kwang-Hyun Cho remarked, "The fact that cancer cells can be converted back to normal cells is an astonishing phenomenon. This study proves that such reversion can be systematically induced."
He further emphasized, "This research introduces the novel concept of reversible cancer therapy by reverting cancer cells to normal cells. It also develops foundational technology for identifying targets for cancer reversion through the systematic analysis of normal cell differentiation trajectories."
This research included contributions from Jeong-Ryeol Gong, Chun-Kyung Lee, Hoon-Min Kim, Juhee Kim, and Jaeog Jeon, and was published in the online edition of the international journal Advanced Science by Wiley on December 11. (Title: “Control of Cellular Differentiation Trajectories for Cancer Reversion”) DOI: https://doi.org/10.1002/advs.202402132
< Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed a source technology to systematically discover key control factors that can induce reversibility of colon cancer cells through a systems biology approach and a digital twin simulation analysis of the differentiation trajectory of normal colon cells, and verified the effects of reversion on actual colon cancer through molecular cell experiments and animal experiments. >
The study was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Mid-Career Researcher Program and Basic Research Laboratory Program. The research findings have been transferred to BioRevert Inc., where they will be used for the development of practical cancer reversion therapies.
2024.12.23 View 135384 -
KAIST Unveils New Possibilities for Treating Intractable Brain Tumors
< Photo 1. (From left) Professor Heung Kyu Lee, KAIST Department of Biological Sciences, and Dr. Keun Bon Ku >
Immunotherapy, which enhances the immune system's T cell response to eliminate cancer cells, has emerged as a key approach in cancer treatment. However, in the case of glioblastoma, an aggressive and treatment-resistant brain tumor, numerous clinical trials have failed to confirm their efficacy. Korean researchers have recently analyzed the mechanisms that cause T cell exhaustion, which is characterized by a loss of function or a weakened response following prolonged exposure to antigens in such intractable cancers, identifying key control factors in T cell activation and clarifying the mechanisms that enhance therapeutic effectiveness.
KAIST (represented by President Kwang Hyung Lee) announced on the 6th of November that Professor Heung Kyu Lee’s team from the Department of Biological Sciences, in collaboration with the Korea Research Institute of Chemical Technology (represented by President Young Kuk Lee), has confirmed improved survival rates in a glioblastoma mouse model. By removing the inhibitory Fc gamma receptor (FcγRIIB), the research team was able to restore the responsiveness of cytotoxic T cells to immune checkpoint inhibitors, leading to enhanced anticancer activity.
The research team examined the effect of FcγRIIB, an inhibitory receptor recently found in cytotoxic T cells, on tumor-infiltrating T cells and the therapeutic effectiveness of the anti-PD-1 immune checkpoint inhibitor.
< Figure 1. Study results on improved survival rate due to increased antitumor activity of anti-PD-1 treatment in inhibitory Fc gamma receptor(Fcgr2b) ablation mice with murine glioblastoma. >
Their findings showed that deleting FcγRIIB induced the increase of tumor antigen-specific memory T cells, which helps to suppress exhaustion, enhances stem-like qualities, and reactivates T cell-mediated antitumor immunity, particularly in response to anti-PD-1 treatment. Furthermore, FcγRIIB deletion led to an increase in antigen-specific memory T cells that maintained continuous infiltration into the tumor tissue.
This study presents a new therapeutic target for tumors unresponsive to immune checkpoint inhibitors and demonstrates that combining FcγRIIB inhibition with anti-PD-1 treatment can produce synergistic effects, potentially improving therapeutic outcomes for tumors like glioblastoma, which typically show resistance to anti-PD-1 therapy.
< Figure 2. Overview of the study on the enhanced response to anti-PD-1 therapy for glioblastoma brain tumors upon deletion of the inhibitory Fc gamma receptor (FcγRIIB) in tumor microenvironment. When the inhibitory Fc gamma receptor (FcγRIIB) of cytotoxic T cells is deleted, an increase in tumor-specific memory T cells (Ttsms) was observed. In addition, this T cell subset is identified as originating from the tumor-draining lymph nodes(TdLNs) and leads to persistent infiltration into the tumor tissue. Anti-PD-1 therapy leads to an increased anti-tumor immune response via Ttsms, which is confirmed by increased tumor cell toxicity and increased cell division and decreased cell de-migration indices. Ultimately, the increased cytotoxic T cell immune response leads to an increase in the survival rate of glioblastoma. >
Professor Heung Kyu Lee explained, "This study offers a way to overcome clinical failures in treating brain tumors with immune checkpoint therapy and opens possibilities for broader applications to other intractable cancers. It also highlights the potential of utilizing cytotoxic T cells for tumor cell therapy."
The study, led by Dr. Keun Bon Ku of KAIST (currently a senior researcher at the Korea Research Institute of Chemical Technology's Center for Infectious Disease Diagnosis and Prevention), along with Chae Won Kim, Yumin Kim, Byeong Hoon Kang, Jeongwoo La, In Kang, Won Hyung Park, Stephen Ahn, and Sung Ki Lee, was published online on October 26 in the Journal for ImmunoTherapy of Cancer, an international journal in tumor immunology and therapy from the Society for Immunotherapy of Cancer. (Paper title: “Inhibitory Fcγ receptor deletion enhances CD8 T cell stemness increasing anti-PD-1 therapy responsiveness against glioblastoma,” http://dx.doi.org/10.1136/jitc-2024-009449).
This research received support from the National Research Foundation of Korea, the Bio & Medical Technology Development Program, and the Samsung Science & Technology Foundation.
2024.11.15 View 12474 -
KAIST team develops smart immune system that can pin down on malignant tumors
A joint research team led by Professor Jung Kyoon Choi of the KAIST Department of Bio and Brain Engineering and Professor Jong-Eun Park of the KAIST Graduate School of Medical Science and Engineering (GSMSE) announced the development of the key technologies to treat cancers using smart immune cells designed based on AI and big data analysis. This technology is expected to be a next-generation immunotherapy that allows precision targeting of tumor cells by having the chimeric antigen receptors (CARs) operate through a logical circuit. Professor Hee Jung An of CHA Bundang Medical Center and Professor Hae-Ock Lee of the Catholic University of Korea also participated in this research to contribute joint effort.
Professor Jung Kyoon Choi’s team built a gene expression database from millions of cells, and used this to successfully develop and verify a deep-learning algorithm that could detect the differences in gene expression patterns between tumor cells and normal cells through a logical circuit. CAR immune cells that were fitted with the logic circuits discovered through this methodology could distinguish between tumorous and normal cells as a computer would, and therefore showed potentials to strike only on tumor cells accurately without causing unwanted side effects.
This research, conducted by co-first authors Dr. Joonha Kwon of the KAIST Department of Bio and Brain Engineering and Ph.D. candidate Junho Kang of KAIST GSMSE, was published by Nature Biotechnology on February 16, under the title Single-cell mapping of combinatorial target antigens for CAR switches using logic gates.
An area in cancer research where the most attempts and advances have been made in recent years is immunotherapy. This field of treatment, which utilizes the patient’s own immune system in order to overcome cancer, has several methods including immune checkpoint inhibitors, cancer vaccines and cellular treatments. Immune cells like CAR-T or CAR-NK equipped with chimera antigen receptors, in particular, can recognize cancer antigens and directly destroy cancer cells.
Starting with its success in blood cancer treatment, scientists have been trying to expand the application of CAR cell therapy to treat solid cancer. But there have been difficulties to develop CAR cells with effective killing abilities against solid cancer cells with minimized side effects. Accordingly, in recent years, the development of smarter CAR engineering technologies, i.e., computational logic gates such as AND, OR, and NOT, to effectively target cancer cells has been underway.
At this point in time, the research team built a large-scale database for cancer and normal cells to discover the exact genes that are expressed only from cancer cells at a single-cell level. The team followed this up by developing an AI algorithm that could search for a combination of genes that best distinguishes cancer cells from normal cells. This algorithm, in particular, has been used to find a logic circuit that can specifically target cancer cells through cell-level simulations of all gene combinations. CAR-T cells equipped with logic circuits discovered through this methodology are expected to distinguish cancerous cells from normal cells like computers, thereby minimizing side effects and maximizing the effects of chemotherapy.
Dr. Joonha Kwon, who is the first author of this paper, said, “this research suggests a new method that hasn’t been tried before. What’s particularly noteworthy is the process in which we found the optimal CAR cell circuit through simulations of millions of individual tumors and normal cells.” He added, “This is an innovative technology that can apply AI and computer logic circuits to immune cell engineering. It would contribute greatly to expanding CAR therapy, which is being successfully used for blood cancer, to solid cancers as well.”
This research was funded by the Original Technology Development Project and Research Program for Next Generation Applied Omic of the Korea Research Foundation.
Figure 1. A schematic diagram of manufacturing and administration process of CAR therapy and of cancer cell-specific dual targeting using CAR.
Figure 2. Deep learning (convolutional neural networks, CNNs) algorithm for selection of dual targets based on gene combination (left) and algorithm for calculating expressing cell fractions by gene combination according to logical circuit (right).
2023.03.09 View 19673 -
Afternoon chemotherapy proved to deliver more desirable results for female lymphoma patients
Chemotherapy is a commonly used regimen for cancer treatment, but it is also a double-edged sword. While the drugs are highly effective at killing cancer cells, they are also notorious for killing healthy cells in the body. As such, minimizing the drug’s damage to the patient’s body is necessary for improving the prognosis of chemotherapy.
Recently, “chrono-chemotherapy” have been gaining interest in the research community. As the name suggests, the aim is timing the delivery of the drugs when the body is least vulnerable to their harmful effects and while the cancer cells are at their most vulnerable.
< Figure 1. Chrono-chemotherapy considering circadian rhythm >
Chrono-chemotherapy exploits the fact that human physiological processes, including cell proliferation and differentiation, are regulated by an endogenous timer called the circadian clock. However, this has not been widely exploited in real-world clinical settings because, as of now, there is no systematic method for finding the optimal chemotherapy delivery time.
This problem was tackled by an interdisciplinary team of researchers from South Korea. They were led by principal investigators Jae Kyoung Kim (a mathematician from the Biomedical Mathematics Group, Institute for Basic Science) and Youngil Koh (an oncologist at Seoul National University Hospital). The researchers studied a group of patients suffering from diffuse large B-cell lymphoma (DLBCL).
Terminology
* Diffuse large B-cell lymphoma (DLBCL): Lymphoma is a type of blood cancer caused by the malignant transformation of lymphoid tissue cells. Lymphoma is divided into Hodgkin's lymphoma and non-Hodgkin's lymphoma (malignant lymphoma), and diffuse large B-cell lymphoma accounts for about 30 to 40% of non-Hodgkin's lymphoma.
The research team noticed that DLBCL patients at Seoul National University Hospital received chemotherapy on two different schedules, with some patients receiving morning treatment (8:30 a.m.) and others taking the drugs in the afternoon (2:30 p.m.). All patients received the same cancer treatment (R-CHOP), which is a combination of targeted therapy and chemotherapy, four to six times in the morning or afternoon at intervals of about three weeks.
They analyzed 210 patients to investigate whether there was any difference between morning and afternoon treatments. It was found that female patients who received the afternoon treatment had a 12.5 times reduced mortality rate (25% to 2%), while the cancer recurrence after 60 months decreased by 2.8 times (37% to 13%). In addition, chemotherapy side effects such as neutropenia were more common in female patients who received the morning treatment.
Surprisingly, there was no differences found in treatment efficiency depending on the treatment schedule in the cases of male patients.
To understand the cause of the gender differences, the research team analyzed upto 14,000 blood samples from the Seoul National University Hospital Health Examination Center. It was found that in females, white blood cell counts tended to decrease in the morning and increase in the afternoon. This indicates that the bone marrow proliferation rate was higher in the morning than in the afternoon because there is a upto 12 hour delay between bone marrow proliferation and blood cell production.
This means that if a female patient receives chemotherapy in the morning when bone marrow is actively producing blood cells, the possibility of adverse side effects becomes greater. These results are consistent with the findings from recent randomized clinical trials that showed female colorectal cancer patients treated with irinotecan in the morning suffered from higher drug toxicities.
One confounding variable was the drug dose. Since the morning female patients suffered from greater adverse side effects, oftentimes the dose had to be reduced for these patients. On average, the drug dose was reduced by upto 10% compared to the dose intensity given to female patients receiving the afternoon treatment.
Unlike the female patients, it was found that male patients did not show a significant difference in white blood cell count and bone marrow cell proliferation activity throughout the day, which explains why the timing of the treatment had no impact.
Professor Youngil Koh said, “We plan to verify the conclusions of this study again with a large-scale follow-up study that completely controls for the confounding variables, and to confirm whether chrono-chemotherapy has similar effects on other cancers.”
CI Jae Kyoung Kim said, “Because the time of the internal circadian clock can vary greatly depending on the individual's sleep-wake patterns, we are currently developing a technology to estimate a patient’s circadian clock from their sleep pattern. We hope that this can be used to develop an individualized anti-cancer chronotherapy schedule.”
< Figure 2. Chemotherapy in the afternoon can improve treatment outcomes. >
The daily fluctuation of proliferative activity of bone marrow is larger in females than in males, and it becomes higher in the morning (left). Thus, chemotherapy in the morning strongly inhibits proliferative activity in female lymphoma patients, resulting in a higher incidence of adverse events such as neutropenia and infections. This forced the clinicians to reduce the dose intensity (center). Consequently, female patients undergoing the morning treatment showed a lower survival probability than those undergoing the afternoon treatment (right). Specifically, only ~13% of female patients treated in the afternoon had a worse outcome and ~2% of them died while ~37% of female patients treated in the morning had a worse outcome and ~25% of them died. Male patients did not show any difference in treatment outcomes depending on the chemotherapy delivery time.
2023.01.27 View 12616 -
Identification of How Chemotherapy Drug Works Could Deliver Personalized Cancer Treatment
The chemotherapy drug decitabine is commonly used to treat patients with blood cancers, but its response rate is somewhat low. Researchers have now identified why this is the case, opening the door to more personalized cancer therapies for those with these types of cancers, and perhaps further afield.
Researchers have identified the genetic and molecular mechanisms within cells that make the chemotherapy drug decitabine—used to treat patients with myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) —work for some patients but not others. The findings should assist clinicians in developing more patient-specific treatment strategies.
The findings were published in the Proceedings of the National Academies of Science on March 30.
The chemotherapy drug decitabine, also known by its brand name Dacogen, works by modifying our DNA that in turn switches on genes that stop the cancer cells from growing and replicating. However, decitabine’s response rate is somewhat low (showing improvement in just 30-35% of patients), which leaves something of a mystery as to why it works well for some patients but not for others. To find out why this happens, researchers from the KAIST investigated the molecular mediators that are involved with regulating the effects of the drug.
Decitabine works to activate the production of endogenous retroviruses (ERVs), which in turn induces an immune response. ERVs are viruses that long ago inserted dormant copies of themselves into the human genome. Decitabine in essence, ‘reactivates’ these viral elements and produces double-stranded RNAs (dsRNAs) that the immune system views as a foreign body.
“However, the mechanisms involved in this process, in particular how production and transport of these ERV dsRNAs were regulated within the cell were understudied,” said corresponding author Yoosik Kim, professor in the Department of Chemical and Biomolecular Engineering at KAIST.
“So to explain why decitabine works in some patients but not others, we investigated what these molecular mechanisms were,” added Kim.
To do so, the researchers used image-based RNA interference (RNAi) screening. This is a relatively new technique in which specific sequences within a genome are knocked out of action or “downregulated.” Large-scale screening, which can be performed in cultured cells or within live organisms, works to investigate the function of different genes. The KAIST researchers collaborated with the Institut Pasteur Korea to analyze the effect of downregulating genes that recognize ERV dsRNAs and could be involved in the cellular response to decitabine.
From these initial screening results, they performed an even more detailed downregulation screening analysis. Through the screening, they were able to identify two particular gene sequences involved in the production of an RNA-binding protein called Staufen1 and the production of a strand of RNA that does not in turn produce any proteins called TINCR that play a key regulatory role in response to the drug. Staufen1 binds directly to dsRNAs and stabilizes them in concert with the TINCR.
If a patient is not producing sufficient Staufen1 and TINCR, then the dsRNA viral mimics quickly degrade before the immune system can spot them. And, crucially for cancer therapy, this means that patients with lower expression (activation) of these sequences will show inferior response to decitabine. Indeed, the researchers confirmed that MDS/AML patients with low Staufen1 and TINCR expression did not benefit from decitabine therapy.
“We can now isolate patients who will not benefit from the therapy and direct them to a different type of therapy,” said first author Yongsuk Ku. “This serves as an important step toward developing a patient-specific treatment cancer strategy.”
As the researchers used patient samples taken from bone marrow, the next step will be to try to develop a testing method that can identify the problem from just blood samples, which are much easier to acquire from patients.
The team plans to investigate if the analysis can be extended to patients with solid tumors in addition to those with blood cancers.
-Profile
Professor Yoosik Kim
https://qcbio.kaist.ac.kr/
Department of Chemical and Biomolecular Engineering
KAIST
-Publication
Noncanonical immune response to the inhibition of DNA methylation by Staufen1 via stabilization of endogenous retrovirus RNAs, PNAS
2021.05.24 View 16865 -
Deep-Learning and 3D Holographic Microscopy Beats Scientists at Analyzing Cancer Immunotherapy
Live tracking and analyzing of the dynamics of chimeric antigen receptor (CAR) T-cells targeting cancer cells can open new avenues for the development of cancer immunotherapy. However, imaging via conventional microscopy approaches can result in cellular damage, and assessments of cell-to-cell interactions are extremely difficult and labor-intensive. When researchers applied deep learning and 3D holographic microscopy to the task, however, they not only avoided these difficultues but found that AI was better at it than humans were.
Artificial intelligence (AI) is helping researchers decipher images from a new holographic microscopy technique needed to investigate a key process in cancer immunotherapy “live” as it takes place. The AI transformed work that, if performed manually by scientists, would otherwise be incredibly labor-intensive and time-consuming into one that is not only effortless but done better than they could have done it themselves. The research, conducted by the team of Professor YongKeun Park from the Department of Physics, appeared in the journal eLife last December.
A critical stage in the development of the human immune system’s ability to respond not just generally to any invader (such as pathogens or cancer cells) but specifically to that particular type of invader and remember it should it attempt to invade again is the formation of a junction between an immune cell called a T-cell and a cell that presents the antigen, or part of the invader that is causing the problem, to it. This process is like when a picture of a suspect is sent to a police car so that the officers can recognize the criminal they are trying to track down. The junction between the two cells, called the immunological synapse, or IS, is the key process in teaching the immune system how to recognize a specific type of invader.
Since the formation of the IS junction is such a critical step for the initiation of an antigen-specific immune response, various techniques allowing researchers to observe the process as it happens have been used to study its dynamics. Most of these live imaging techniques rely on fluorescence microscopy, where genetic tweaking causes part of a protein from a cell to fluoresce, in turn allowing the subject to be tracked via fluorescence rather than via the reflected light used in many conventional microscopy techniques.
However, fluorescence-based imaging can suffer from effects such as photo-bleaching and photo-toxicity, preventing the assessment of dynamic changes in the IS junction process over the long term. Fluorescence-based imaging still involves illumination, whereupon the fluorophores (chemical compounds that cause the fluorescence) emit light of a different color. Photo-bleaching or photo-toxicity occur when the subject is exposed to too much illumination, resulting in chemical alteration or cellular damage.
One recent option that does away with fluorescent labelling and thereby avoids such problems is 3D holographic microscopy or holotomography (HT). In this technique, the refractive index (the way that light changes direction when encountering a substance with a different density—why a straw looks like it bends in a glass of water) is recorded in 3D as a hologram.
Until now, HT has been used to study single cells, but never cell-cell interactions involved in immune responses. One of the main reasons is the difficulty of “segmentation,” or distinguishing the different parts of a cell and thus distinguishing between the interacting cells; in other words, deciphering which part belongs to which cell.
Manual segmentation, or marking out the different parts manually, is one option, but it is difficult and time-consuming, especially in three dimensions. To overcome this problem, automatic segmentation has been developed in which simple computer algorithms perform the identification.
“But these basic algorithms often make mistakes,” explained Professor YongKeun Park, “particularly with respect to adjoining segmentation, which of course is exactly what is occurring here in the immune response we’re most interested in.”
So, the researchers applied a deep learning framework to the HT segmentation problem. Deep learning is a type of machine learning in which artificial neural networks based on the human brain recognize patterns in a way that is similar to how humans do this. Regular machine learning requires data as an input that has already been labelled. The AI “learns” by understanding the labeled data and then recognizes the concept that has been labelled when it is fed novel data. For example, AI trained on a thousand images of cats labelled “cat” should be able to recognize a cat the next time it encounters an image with a cat in it. Deep learning involves multiple layers of artificial neural networks attacking much larger, but unlabeled datasets, in which the AI develops its own ‘labels’ for concepts it encounters.
In essence, the deep learning framework that KAIST researchers developed, called DeepIS, came up with its own concepts by which it distinguishes the different parts of the IS junction process. To validate this method, the research team applied it to the dynamics of a particular IS junction formed between chimeric antigen receptor (CAR) T-cells and target cancer cells. They then compared the results to what they would normally have done: the laborious process of performing the segmentation manually. They found not only that DeepIS was able to define areas within the IS with high accuracy, but that the technique was even able to capture information about the total distribution of proteins within the IS that may not have been easily measured using conventional techniques.
“In addition to allowing us to avoid the drudgery of manual segmentation and the problems of photo-bleaching and photo-toxicity, we found that the AI actually did a better job,” Professor Park added.
The next step will be to combine the technique with methods of measuring how much physical force is applied by different parts of the IS junction, such as holographic optical tweezers or traction force microscopy.
-Profile
Professor YongKeun Park
Department of Physics
Biomedical Optics Laboratory
http://bmol.kaist.ac.kr
KAIST
2021.02.24 View 19166 -
Biomarker Predicts Who Will Have Severe COVID-19
- Airway cell analyses showing an activated immune axis could pinpoint the COVID-19 patients who will most benefit from targeted therapies.-
KAIST researchers have identified key markers that could help pinpoint patients who are bound to get a severe reaction to COVID-19 infection. This would help doctors provide the right treatments at the right time, potentially saving lives. The findings were published in the journal Frontiers in Immunology on August 28.
People’s immune systems react differently to infection with SARS-CoV-2, the virus that causes COVID-19, ranging from mild to severe, life-threatening responses.
To understand the differences in responses, Professor Heung Kyu Lee and PhD candidate Jang Hyun Park from the Graduate School of Medical Science and Engineering at KAIST analysed ribonucleic acid (RNA) sequencing data extracted from individual airway cells of healthy controls and of mildly and severely ill patients with COVID-19. The data was available in a public database previously published by a group of Chinese researchers.
“Our analyses identified an association between immune cells called neutrophils and special cell receptors that bind to the steroid hormone glucocorticoid,” Professor Lee explained. “This finding could be used as a biomarker for predicting disease severity in patients and thus selecting a targeted therapy that can help treat them at an appropriate time,” he added.
Severe illness in COVID-19 is associated with an exaggerated immune response that leads to excessive airway-damaging inflammation. This condition, known as acute respiratory distress syndrome (ARDS), accounts for 70% of deaths in fatal COVID-19 infections.
Scientists already know that this excessive inflammation involves heightened neutrophil recruitment to the airways, but the detailed mechanisms of this reaction are still unclear.
Lee and Park’s analyses found that a group of immune cells called myeloid cells produced excess amounts of neutrophil-recruiting chemicals in severely ill patients, including a cytokine called tumour necrosis factor (TNF) and a chemokine called CXCL8.
Further RNA analyses of neutrophils in severely ill patients showed they were less able to recruit very important T cells needed for attacking the virus. At the same time, the neutrophils produced too many extracellular molecules that normally trap pathogens, but damage airway cells when produced in excess.
The researchers additionally found that the airway cells in severely ill patients were not expressing enough glucocorticoid receptors. This was correlated with increased CXCL8 expression and neutrophil recruitment.
Glucocorticoids, like the well-known drug dexamethasone, are anti-inflammatory agents that could play a role in treating COVID-19. However, using them in early or mild forms of the infection could suppress the necessary immune reactions to combat the virus. But if airway damage has already happened in more severe cases, glucocorticoid treatment would be ineffective.
Knowing who to give this treatment to and when is really important. COVID-19 patients showing reduced glucocorticoid receptor expression, increased CXCL8 expression, and excess neutrophil recruitment to the airways could benefit from treatment with glucocorticoids to prevent airway damage. Further research is needed, however, to confirm the relationship between glucocorticoids and neutrophil inflammation at the protein level.
“Our study could serve as a springboard towards more accurate and reliable COVID-19 treatments,” Professor Lee said.
This work was supported by the National Research Foundation of Korea, and Mobile Clinic Module Project funded by KAIST.
Figure. Low glucocorticoid receptor (GR) expression led to excessive inflammation and lung damage by neutrophils through enhancing the expression of CXCL8 and other cytokines.
Image credit: Professor Heung Kyu Lee, KAIST. Created with Biorender.com.
Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only.
-Publication:
Jang Hyun Park, and Heung Kyu Lee. (2020). Re-analysis of Single Cell Transcriptome Reveals That the NR3C1-CXCL8-Neutrophil Axis Determines the Severity of COVID-19. Frontiers in Immunology, Available online at https://doi.org/10.3389/fimmu.2020.02145
-Profile: Heung Kyu Lee
Associate Professor
heungkyu.lee@kaist.ac.kr
https://www.heungkyulee.kaist.ac.kr/
Laboratory of Host Defenses
Graduate School of Medical Science and Engineering (GSMSE)
The Center for Epidemic Preparedness at KAIST Institute
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
Profile: Jang Hyun Park
PhD Candidate
janghyun.park@kaist.ac.kr
GSMSE, KAIST
2020.09.17 View 25467 -
Sungjoon Park Named Google PhD Fellow
PhD candidate Sungjoon Park from the School of Computing was named a 2019 Google PhD Fellow in the field of natural language processing. The Google PhD fellowship program has recognized and supported outstanding graduate students in computer science and related fields since 2009. Park is one of three Korean students chosen as the recipients of Google Fellowships this year. A total of 54 students across the world in 12 fields were awarded this fellowship.
Park’s research on computational psychotherapy using natural language processing (NLP) powered by machine learning earned him this year’s fellowship. He presented of learning distributed representations in Korean and their interpretations during the 2017 Annual Conference of the Association for Computational Linguistics and the 2018 Conference on Empirical Methods in Natural Language Processing. He also applied machine learning-based natural language processing into computational psychotherapy so that a trained machine learning model could categorize client's verbal responses in a counseling dialogue. This was presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics.
More recently, he has been developing on neural response generation model and the prediction and extraction of complex emotion in text, and computational psychotherapy applications.
2019.09.17 View 16169