Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data

Gim, Jungsoo,Kim, Wonji,Kwak, Soo Heon,Choi, Hosik,Park, Changyi,Park, Kyong Soo,Kwon, Sunghoon,Park, Taesung,Won, Sungho The Genetics Society of America 2017 Genetics Vol.207 No.3

Predicting multi-class responses to preoperative chemoradiotherapy in rectal cancer patients

Gim, Jungsoo,Cho, Yong Beom,Hong, Hye Kyung,Kim, Hee Cheol,Yun, Seong Hyeon,Wu, Hong-Gyun,Jeong, Seung-Yong,Joung, Je-Gun,Park, Taesung,Park, Woong-Yang,Lee, Woo Yong BioMed Central 2016 Radiation oncology Vol.11 No.-

<P><B>Background</B></P><P>Preoperative chemoradiotherapy (CRT) has become a widely used treatment for improving local control of disease and increasing survival rates of rectal cancer patients. We aimed to identify a set of genes that can be used to predict responses to CRT in patients with rectal cancer.</P><P><B>Methods</B></P><P>Gene expression profiles of pre-therapeutic biopsy specimens obtained from 77 rectal cancer patients were analyzed using DNA microarrays. The response to CRT was determined using the Dworak tumor regression grade: grade 1 (minimal, MI), grade 2 (moderate, MO), grade 3 (near total, NT), or grade 4 (total, TO).</P><P><B>Results</B></P><P>Top ranked genes for three different feature scores such as a <I>p</I>-value (pval), a rank product (rank), and a normalized product (norm) were selected to distinguish pre-defined groups such as complete responders (TO) from the MI, MO, and NT groups. Among five different classification algorithms, supporting vector machine (SVM) with the top 65 norm features performed at the highest accuracy for predicting MI using a 5-fold cross validation strategy. On the other hand, 98 pval features were selected for predicting TO by elastic net (EN). Finally we combined TO- and MI-finder models to build a three-class classification model and validated it using an independent dataset of rectal cancer mRNA expression.</P><P><B>Conclusions</B></P><P>We identified MI- and TO-finders for predicting preoperative CRT responses, and validated these data using an independent public dataset. This stepwise prediction model requires further evaluation in clinical studies in order to develop personalized preoperative CRT in patients with rectal cancer.</P><P><B>Electronic supplementary material</B></P><P>The online version of this article (doi:10.1186/s13014-016-0623-9) contains supplementary material, which is available to authorized users.</P>

A comparative investigation of single nucleotide variant calling for a personal non-Caucasian sequencing sample

Park HyeonSeul,Gim JungSoo 한국유전학회 2023 Genes & Genomics Vol.45 No.12

Background Dropping cost and increasing clinical application of whole genome sequencing (WGS) lead a necessity of efficient (accurate and rapid) variant calling procedures from a personal WGS data (n = 1). A number of variant calling pipelines have been introduced utilizing the human genome reference GRCh38 as a reference and a benchmark dataset called ‘NA12878’, which are both ‘standard’ but limited ethnic origin. Considering the nature of variant calling algorithms and recent updates in sequencing protocol, however, it is necessary to revisit the efficiency of the current best pipelines for a personal WGS data from diverse ethnicity. Objective We discuss the most efficient practices for variant calling of a personal WGS reads, with a particular emphasis on whether (1) ethnic match or mismatch between the reference genome and a WGS data produces a distinct result and more importantly (2) there is an ethnic-specific optimal workflow. Methods Here, we generate an appropriate WGS data, DNA array, and sufficient number of Sanger validated variants from a single Korean subject to perform such a comprehensive comparison. We applied this WGS reads and the ‘NA12878’ reads to 8 different variant calling pipelines with 2 different reference genomes (GRCh38 and KOREF, a Korean reference genome) to which the WGS reads from different ethnic origins are aligned. Results We evaluated the performance of the pipelines with the matched array genotype data and Sanger sequencing validation and demonstrated that: regardless to the ethnic match/mismatch (1) Novoalign-GATK4 showed the most efficient performance with the exceptional calls in MHC region; (2) the overall performance was better with GRCh38, while a significant difference in recall was observed. In addition, we found it is largely reduced computing cost maintaining performance to remove ‘markduplication’ step with PCR-free WGS data. Conclusion For variant calling of a personal PCR-free WGS data, regardless of ethnicity consideration, we recommend the use of the Novoalign + GATK4 with GRCh38 and without ‘markduplication’.

Heat Shock Factor 1 Predicts Poor Prognosis of Gastric Cancer

Kim, Seok-Jun,Lee, Seok-Cheol,Kang, Hyun-Gu,Gim, Jungsoo,Lee, Kyung-Hwa,Lee, Seung-Hyun,Chun, Kyung-Hee Yonsei University, College of Medicine 2018 Yonsei medical journal Vol.59 No.9

<P><B>Purpose</B></P><P>Heat shock factor 1 (HSF1) is a key regulator of the heat shock response and plays an important role in various cancers. However, the role of HSF1 in gastric cancer is still unknown. The present study evaluated the function of HSF1 and related mechanisms in gastric cancer.</P><P><B>Materials and Methods</B></P><P>The expression levels of HSF1 in normal and gastric cancer tissues were compared using cDNA microarray data from the NCBI Gene Expression Omnibus (GEO) dataset. The proliferation of gastric cancer cells was analyzed using the WST assay. Transwell migration and invasion assays were used to evaluate the migration and invasion abilities of gastric cancer cells. Protein levels of HSF1 were analyzed using immunohistochemical staining of tissue microarrays from patients with gastric cancer.</P><P><B>Results</B></P><P>HSF1 expression was significantly higher in gastric cancer tissue than in normal tissue. Knockdown of HSF1 reduced the proliferation, migration, and invasion of gastric cancer cells, while HSF1 overexpression promoted proliferation, migration, and invasion of gastric cancer cells. Furthermore, HSF1 promoted the proliferation of gastric cancer cells <I>in vivo</I>. In Kaplan-Meier analysis, high levels of HSF1 were associated with poor prognosis for patients with gastric cancer (<I>p</I>=0.028).</P><P><B>Conclusion</B></P><P>HSF1 may be closely associated with the proliferation and motility of gastric cancer cells and poor prognosis of patients with gastric cancer. Accordingly, HSF1 could serve as a prognostic marker for gastric cancer.</P>

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study

Yu, Su Jong,Kim, Hyunsoo,Min, Hophil,Sohn, Areum,Cho, Young Youn,Yoo, Jeong-Ju,Lee, Dong Hyeon,Cho, Eun Ju,Lee, Jeong-Hoon,Gim, Jungsoo,Park, Taesung,Kim, Yoon Jun,Kim, Chung Yong,Yoon, Jung-Hwan,Kim, American Chemical Society 2017 Journal of proteome research Vol.16 No.3

<P>This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.</P>

A Multimarker Panel Predicts Complete Response after Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma

( Su Jong Yu ),( Hyunsoo Kim ),( Hophil Min ),( Areum Sohn ),( Young Youn Cho ),( Jeong-ju Yoo ),( Dong Hyeon Lee ),( Eun Ju Cho ),( Jeong-hoon Lee ),( Jungsoo Gim ),( Taesung Park ),( Yoon Jun Kim ) 대한간학회 2016 춘·추계 학술대회 (KASL) Vol.2016 No.1

Aims: Achievement of a complete response (CR) after transarterial chemoembolization (TACE) is the most robust predictor of favorable outcomes in patients with hepatocellular carcinoma (HCC). The aim of this study was to identify blood-based biomarkers to predict a sustained CR after TACE using targeted proteomics. Methods: Consecutive patients with HCC who had undergone TACE were drawn from our prospective cohort [training set (n=100) and validation set (n=80)]. Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Multiple reaction monitoring-mass spectrometry (MRM-MS) was used to measure marker candidate proteins (MCPs) with regard to their association with the recurrence of HCC and a sustained CR after TACE. Results: In the training set, the MRM-MS assay identified 5 MCPs (MRM-MS marker panel). When this 5-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Further, the ensemble model remained an independent predictor of rapid progression (hazard ratio, 2.889; 95% confidence interval, 1.612-5.178; P<0.001) in the entire population by multivariate analysis. Conclusions: Our ensemble model before TACE can predict a sustained CR after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.