• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권12호
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• pp.547-554
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• 2021

암을 유발하는 유전자는 모든 암 환자에게 공통적인 것은 아니며, 이러한 환자 특이적 암 유발 유전자의 탐색은 개인 맟춤형 암 치료 및 항암제 개발에 있어서 매우 중요하다. 환자 특이적 암 유발 유전자를 찾기 위한 생물 정보학 연구들이 있어왔지만, 아직 정확도 면에서는 발전의 여지가 있다. 본 논문에서는 환자 특이적 암 유발 유전자를 탐색하기 위하여 NPD (Network based Patient-specific Driver gene identification)라는 방법을 제안한다. NPD는 환자 특이적 유전자 네트워크를 구축하고, 여기에 수정된 PageRank 알고리즘을 적용하여 유전자에 점수를 부여한 후, 유전적 변이 데이터를 사용한 승률 계산 방법을 통하여 암 유발 유전자를 찾는 세 단계로 이루어진다. TCGA 데이터 베이스의 여섯 개의 암 데이터에 NPD를 적용한 결과, NPD가 기존의 환자 특이적 암 유발 유전자 탐색 방법들보다 전체적으로 높은 F1 점수를 보여줌을 확인할 수 있었다.

• Asian Pacific Journal of Cancer Prevention
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• 제17권7호
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• pp.3377-3380
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• 2016

The proportion of lung cancer patients under 50 years old is small at approximately 5-10%, but as with patients older than 50, the number is on the rise. Although lung cancer treatment strategies have undergone extensive transformation in recent years based on the presence or absence of oncogenic driver mutations, there are few reports regarding these mutations in the young or the relationship between clinical setting and prognosis. Therefore, we conducted a study of clinical features in 36 patients under the age of 50 who were diagnosed with primary lung cancer from October 2008 to November 2015. The 22 patients in stages I through III A underwent operations, and all 17 whose lung cancer were detected through screening were candidates for surgery. Gene analysis was conducted for 26 (72.2%); 10 (38.5%) were positive for EGFR gene mutations, and ALK gene translocation was present in 4 (15.4%). In stage IV patients, the median progression free survival (PFS) in the ALK translocation positive and negative patients was 518 days and 130 days, respectively, and the median overall survival (OS) was not reached and 280 days, respectively. A trend toward extended PFS (p=0.203) and OS (p=0.056) was observed in patients positive for ALK translocation. We must strive for early detection by increasing screening rates and evaluate oncogenic driver mutations important for prognosis of lung cancer in the young.

• Asian Pacific Journal of Cancer Prevention
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• 제16권10호
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• pp.4147-4156
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• 2015

Lung cancer is a serious health problem and leading cause of death worldwide due to its high incidence and mortality. More than 80% of lung cancers feature a non-small cell histology. Over few decades, systemic chemotherapy and surgery are the only treatment options in this type of tumor but due to their limited efficacy and overall poor survival of patients, there is an urge to develop newer therapeutic strategies which circumvent the problems. Enhanced knowledge of translational science and molecular biology have revealed that lung tumors carry diverse driver gene mutations and adopt different intracellular pathways leading to carcinogenesis. Hence, the development of targeted agents against molecular subgroups harboring critical mutations is an attractive approach for therapeutic treatment. Targeted therapies are clearly more preferred nowadays over systemic therapies because they target tumor specific molecules resulting with enhanced activity and reduced toxicity to normal tissues. Thus, this review encompasses comprehensive updates on targeted therapies for the driver mutations in non-small cell lung cancer (NSCLC) and the potential challenges of acquired drug resistance faced i n the field of targeted therapy along with the imminent newer treatment modalities against lung cancer.

• Genomics & Informatics
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• 제10권1호
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• BMB Reports
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• 제51권2호
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• pp.57-58
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• 2018

An accurate diagnostic marker for detecting early-stage hepatocellular carcinoma (eHCC) is clinically important, since early detection of HCC remarkably improves patient survival. From the integrative analysis of the transcriptome and clinicopathologic data of human multi-stage HCC tissues, we were able to identify barrier-to-autointegration factor 1 (BANF1), procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 (PLOD3) and splicing factor 3b subunit 4 (SF3B4) as early HCC biomarkers which could be detected in precancerous lesions of HCC, with superior capabilities to diagnose eHCC compared to the currently popular HCC diagnostic biomarkers: GPC3, GS, and HSP70. We then showed that SF3B4 knockdown caused G1/S cell cycle arrest by recovering $p27^{kip1}$ and simultaneously suppressing cyclins, and CDKs in liver cancer cells. Notably, we demonstrated that aberrant SF3B4 overexpression altered the progress of splicing progress of the tumor suppressor gene, kruppel like factor 4 (KLF4), and resulted in non-functional skipped exon transcripts. This contributes to liver tumorigenesis via transcriptional inactivation of $p27^{kip1}$ and simultaneous activation of Slug genes. Our results suggest that SF3B4 indicates early-stage HCC in precancerous lesions, and also functions as an early-stage driver in the development of liver cancer.

• Genomics & Informatics
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• 제11권4호
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• pp.180-185
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• 2013

Development of liver cancers is driven largely by genomic alterations that deregulate signaling pathways, influencing growth and survival of cancer cells. Because of the hundreds or thousands of genomic/epigenomic alterations that have accumulated in the cancer genome, it is very challenging to find and test candidate genes driving tumor development and progression. Systematic studies of the liver cancer genome have become available in recent years. These studies have uncovered new potential driver genes, including those not previously known to be involved in the development of liver cancer. Novel approaches combining multiple datasets from patient tissues have created an unparalleled opportunity to uncover potential new therapeutic targets and prognostic/predictive biomarkers for personalized therapy that can improve clinical outcomes of the patients with liver cancer.

• Journal of Gastric Cancer
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• 제13권3호
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• pp.129-135
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• 2013

Gastric cancer is the second leading cause of cancer-related deaths worldwide. In advanced and metastatic gastric cancer, the conventional chemotherapy with limited efficacy shows an overall survival period of about 10 months. Patient specific and effective treatments known as personalized cancer therapy is of significant importance. Advances in high-throughput technologies such as microarray and next generation sequencing for genes, protein expression profiles and oncogenic signaling pathways have reinforced the discovery of treatment targets and personalized treatments. However, there are numerous challenges from cancer target discoveries to practical clinical benefits. Although there is a flood of biomarkers and target agents, only a minority of patients are tested and treated accordingly. Numerous molecular target agents have been under investigation for gastric cancer. Currently, targets for gastric cancer include the epidermal growth factor receptor family, mesenchymal-epithelial transition factor axis, and the phosphatidylinositol 3-kinase-AKT-mammalian target of rapamycin pathways. Deeper insights of molecular characteristics for gastric cancer has enabled the molecular classification of gastric cancer, the diagnosis of gastric cancer, the prediction of prognosis, the recognition of gastric cancer driver genes, and the discovery of potential therapeutic targets. Not only have we deeper insights for the molecular diversity of gastric cancer, but we have also prospected both affirmative potentials and hurdles to molecular diagnostics. New paradigm of transdisciplinary team science, which is composed of innovative explorations and clinical investigations of oncologists, geneticists, pathologists, biologists, and bio-informaticians, is mandatory to recognize personalized target therapy.

• Molecules and Cells
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• 제45권5호
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• pp.329-342
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• 2022

The liver is the predominant metastatic site for pancreatic cancer. However, the factors that determine the liver metastasis and the specific molecular mechanisms are still unclear. In this study, we used human pancreatic cancer cell line Hs766T to establish Hs766T-L3, a subline of Hs766T with stable liver metastatic ability. We performed RNA sequencing of Hs766T-L3 and its parental cell line Hs766T, and revealed huge differences in gene expression patterns and pathway activation between these two cell lines. We correlated the difference in pathway activation with the expression of the four core transcriptional factors including STAT1, NR2F2, GATA2, and SMAD4. Using the TCGA database, we examined the relative expression of these transcription factors (TFs) in pan-cancer and their relationship with the prognosis of the pancreatic cancer. Among these TFs, we considered GATA2 is closely involved in tumor metastasis and may serve as a potential metastatic driver. Further in vitro and in vivo experiments confirmed that GATA2-mediated transcriptional activation of Notch3 promotes the liver metastasis of Hs766T-L3, and knockdown of either GATA2 or Notch3 reduces the metastatic ability of Hs766T-L3. Therefore, we claim that GATA2 may serve as a metastatic driver of pancreatic cancer and a potential therapeutic target to treat liver metastasis of pancreatic cancer.

• Genomics & Informatics
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• 제14권3호
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• pp.78-84
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• 2016

Extranodal natural killer (NK)/T-cell lymphoma, nasal type (NKTCL), is a malignant disorder of cytotoxic lymphocytes of NK or T cells. It is an aggressive neoplasm with a very poor prognosis. Although extranodal NKTCL reportedly has a strong association with Epstein-Barr virus, the molecular pathogenesis of NKTCL has been unexplored. The recent technological advancements in next-generation sequencing (NGS) have made DNA sequencing cost- and time-effective, with more reliable results. Using the Ion Proton Comprehensive Cancer Panel, we sequenced 409 cancer-related genes to identify somatic mutations in five NKTCL tissue samples. The sequencing analysis detected 25 mutations in 21 genes. Among them, KMT2D, a histone modification-related gene, was the most frequently mutated gene (four of the five cases). This result was consistent with recent NGS studies that have suggested KMT2D as a novel driver gene in NKTCL. Mutations were also found in ARID1A, a chromatin remodeling gene, and TP53, which also recurred in recent NGS studies. We also found mutations in 18 novel candidate genes, with molecular functions that were potentially implicated in cancer development. We suggest that these genes may result in multiple oncogenic events and may be used as potential bio-markers of NKTCL in the future.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.490-492
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• 2023

시퀀싱(sequencing) 기술의 발달로 다양한 오믹스(omics) 데이터의 축적과 인공 지능 기술의 발달로 인하여 다양한 드라이버 유전자 분류기법이 제안되어왔다. 최근에는 암 데이터가 대용량으로 축적되며 기계 학습 기반의 다양한 기법들이 활발히 제안되었다. 특히 다양한 오믹스 데이터를 결합한 고차원 데이터에서 높은 정확도를 확보하기 위한 시도가 활발히 이루어지고 있다. 본 논문에서는 멀티 오믹스와 네트워크 관련 특징을 기반으로 암의 증식 및 발생에 중요한 역할을 하는 드라이버 유전자를 분류하는 딥러닝 모델을 제시한다. 또한 The Cancer Genome Atlas(TCGA) 데이터를 통해서 모델 학습 후 기존 통계 및 머신러닝 기반 기법과 비교하여 성능이 개선되었음을 확인하였다.