Search

S6. Relative abundance of CM and CMM gene pairs across tumor types.

Table S1. Top 10 significant molecular terms (MSigDB c2cp; Curated Pathways) enriched in correlated and inversely-anticorrelated genes with tumor purity. Table S2. The GSEA results from individual tumor types. Table S3. Immune cell type-specific genes. Table S4. GSEA of genes with high/low differential correlation coefficients.

Surgical archives of tumor specimens are often impure. The presence of RNA transcripts from nontumor cells, such as immune and stromal cells, can impede analyses of cancer expression profiles. To systematically analyze the impact of tumor purity, the gene expression profiles and tumor purities were obtained for 7,794 tumor specimens across 21 tumor types (available in The Cancer Genome Atlas consortium). First, we observed that genes with roles in immunity and oxidative phosphorylation were significantly inversely correlated and correlated with the tumor purity, respectively. The expression of genes implicated in immunotherapy and specific immune cell genes, along with the abundance of immune cell infiltrates, was substantially inversely correlated with tumor purity. This relationship may explain the correlation between immune gene expression and mutation burden, highlighting the need to account for tumor purity in the evaluation of expression markers obtained from bulk tumor transcriptome data. Second, examination of cluster membership of gene pairs, with or without controlling for tumor purity, revealed that tumor purity may have a substantial impact on gene clustering across tumor types. Third, feature genes for molecular taxonomy were analyzed for correlation with tumor purity, and for some tumor types, feature genes representing the mesenchymal and classical subtypes were inversely correlated and correlated with tumor purity, respectively. Our findings indicate that tumor purity is an important confounder in evaluating the correlation between gene expression and clinicopathologic features such as mutation burden, as well as gene clustering and molecular taxonomy. Cancer Immunol Res; 6(1); 87–97. ©2017 AACR.

Supplementary Fig. S1. Schematic protocol of tissue collection in five colon cancers with liver metastasis. In each colon cancer patient, 2 to 5 tumor fragments each apart at least from 0.5 cm were collected from colon. In parallel, 2 to 6 separate metastatic nodules in the liver were collected from each patient.

Purpose: The intratumoral heterogeneity (ITH) and the evolution of genomic architectures associated with the development of distant metastases are not well understood in colorectal cancers.Experimental Design: We performed multiregion biopsies of primary and liver metastatic regions from five colorectal cancers with whole-exome sequencing and copy number profiling.Results: In addition to a substantial level of genetic ITH, multiregion genetic profiling identifies the subclonal mutational architecture, leading to the region-based or spatial categorization of somatic mutations and the inference of intratumoral evolutionary history of cancers. The universal mutations (those observed in all the regional biopsies) are enriched in known cancer genes such as APC and TP53 with distinct mutational spectra compared with biopsy- or region-specific mutations, suggesting that major operative mutational mechanisms and their selective pressures are not constant across the metastatic progression. The phylogenies inferred from genomic data show branching evolutionary patterns where some primary biopsies are often segregated with metastastic lesions. Our analyses also revealed that copy number changes such as the chromosomal gains of c-MYC and chromothripsis can be region specific and the potential source of genetic ITH.Conclusions: Our data show that the genetic ITH is prevalent in colorectal cancer serving as a potential driving force to generate metastasis-initiating clones and also as a means to infer the intratumoral evolutionary history of cancers. The paucity of recurrent metastasis-clonal events suggests that colorectal cancer distant metastases may not follow a uniform course of genomic evolution, which should be considered in the genetic diagnosis and the selection of therapeutic targets for the advanced colorectal cancer. Clin Cancer Res; 21(19); 4461–72. ©2015 AACR.

Supplementary Table 1. Clinicopathological and sequencing information of the cases and regional biopsies

Supplementary Table 2. The mutations observed at least once for the regional biopsies across five CRC genomes. Genomic coordinates are according to hg19. The sequencechanges (Ref and Alt as reference and mutant sequences, respectively) are shown with the corresponding functional categories as reported by ANNOVAR. The ANNOVARreports are shown in the separate column (Annovar description). The color schemes for SC (spatial categories) are as shown in Main Figure 1. The mutations in the biopsies of primary (black; P) and metastatic lesions (purple; M) are shown in the matrix.

Identification of single-cell subtypes is one of the fundamental processes required to understand a heterogeneous population composed of multiple cells, based on single-cell RNA sequencing data. Previously, cell subtype identification was mainly carried out by dimension reduction and clustering approaches that grouped cells with similar expressed profiles together. However, for high robustness to noises and systematic annotation of the subtype in each cell, supervised classification approaches have been widely used. Recently, deep neural network (DNN) models have been widely presented in various fields, including biology. By capturing the composite relationship between sample features and target outcomes, a DNN model enables significant performance improvements in biological data mining analyses. In this paper, we constructed a DNN model, called scDAE for single-cell subtype identification combined with representative feature extraction using a multilayer denoising autoencoder (DAE). The feature sets were learned by the DAE and were further tuned by fully connected layers using a softmax classifier. The model was compared against four state-of-the-art cell subtype identification methods and two conventional machine learning algorithms. From multiple tests, scDAE significantly outperformed competing methods especially on data sets having a large number of cell subtypes and noises. Extracted cell features from the proposed model were clearly clustered with respect to subtype. The results of the experiments indicated that our proposed model is effective in identifying single-cell subtypes and molecular signatures representative of each cell subtype. scDAE is publicly available at https://github.com/cbi-bioinfo/scDAE .

BackgroundPheochromocytoma and paraganglioma (PPGL) are tumours that arise from chromaffin cells. Some genetic mutations influence PPGL, among which, those in genes encoding subunits of succinate dehydrogenase (SDHA, SDHB, SDHC and SDHD) and assembly factor (SDHAF2) are the most relevant. However, the risk of metastasis posed by these mutations is not reported except for SDHB and SDHD mutations. This study aimed to update the metastatic risks, considering prevalence and incidence of each SDHx mutation, which were dealt formerly all together.MethodsWe searched EMBASE and MEDLINE and selected 27 articles. The patients included in the studies were divided into three groups depending on the presence of PPGL. We checked the heterogeneity between studies and performed a meta-analysis using Hartung-Knapp-Sidik-Jonkman method based on a random effect model.ResultsThe highest PPGL prevalence was for SDHB mutation, ranging from 23% to 31%, and for SDHC mutation (23%), followed by that for SDHA mutation (16%). The lowest prevalence was for SDHD mutation, ranging from 6% to 8%. SDHAF2 mutation showed no metastatic events. The PPGL incidence showed a tendency similar to that of its prevalence with the highest risk of metastasis posed by SDHB mutation (12%–41%) and the lowest risk by SDHD mutation (~4%).ConclusionThere was no integrated evidence of how SDHx mutations are related to metastatic PPGL. However, these findings suggest that SDHA, SDHB and SDHC mutations are highly associated and should be tested as indicators of metastasis in patients with PPGL.

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

Motivation Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity. Results We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system. Availability The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity.

OBJECTIVE: This study aimed to investigate the underlying pathogenesis of acute unilateral vestibulopathy (AUV) using gene expression profiling combined with bioinformatics analysis. METHODS: Total RNA was extracted from the peripheral blood mononuclear cells of ten AUV patients in the acute phase and from ten controls. The differentially expressed genes (DEGs) between these two groups were screened using microarray analysis with the cut-off criteria (|fold changes| > 1.5 and p-value

Hyperventilation-induced downbeat nystagmus (HV-DBN) has been reported in cerebellar disorders and explained by a loss of the inhibitory cerebellar output via a metabolic effect on cerebellar Ca2+ channels. The aim of this study was to determine the clinical characteristics and underlying pathogenesis of episodic vestibular syndrome (EVS) with HV-DBN. Of 667 patients with EVS, we recruited 22 with HV-DBN and assessed their clinical characteristics, video-oculographic findings, and the results of molecular genetic analyses. The age at symptom onset was 47.5 ± 13.0 years (mean ± SD), and there was a female preponderance (n = 15, 68%). The duration of vertigo/dizziness attacks ranged from minutes to a few days, and 11 patients (50%) fulfilled the diagnostic criteria for vestibular migraine. HV-induced new-onset DBN in 8 patients, while the remaining 14 showed augmentation of spontaneous DBN by HV. The maximum slow-phase velocity of HV-DBN ranged from 2.2 to 11.9°/s, which showed a statistical difference with that of spontaneous DBN (median = 4.95, IQR = 3.68–6.55 vs. median = 1.25, IQR = 0.20–2.15, p

Objectives: The cause of Meniere's disease (MD) is unclear but likely involves genetic and environmental factors. The aim of this study was to investigate the genetic basis underlying MD by screening putative candidate genes for MD.Methods: Sixty-eight patients who met the diagnostic criteria for MD of the Barany Society were included. We performed targeted gene sequencing using next generation sequencing (NGS) panel composed of 45 MD-associated genes. We identified the rare variants causing non-synonymous amino acid changes, stop codons, and insertions/deletions in the coding regions, and excluded the common variants with minor allele frequency >0.01 in public databases. The pathogenicity of the identified variants was analyzed by various predictive tools and protein structural modeling.Results: The average read depth for the targeted regions was 1446.3-fold, and 99.4% of the targeted regions were covered by 20 or more reads, achieving the high quality of the sequencing. After variant filtering, annotation, and interpretation, we identified a total of 15 rare heterozygous variants in 12 (17.6%) sporadic patients. Among them, four variants were detected in familial MD genes (DTNA, FAM136A, DPT), and the remaining 11 in MD-associated genes (PTPN22, NFKB1, CXCL10, TLR2, MTHFR, SLC44A2, NOS3, NOTCH2). Three patients had the variants in two or more genes. All variants were not detected in our healthy controls (n = 100). No significant differences were observed between patients with and without a genetic variant in terms of sex, mean age of onset, bilaterality, the type of MD, and hearing threshold at diagnosis.Conclusions: Our study identified rare variants of putative candidate genes in some of MD patients. The genes were related to the formation of inner ear structures, the immune-associated process, or systemic hemostasis derangement, suggesting the multiple genetic predispositions in the development of MD.

DNA methylation leads to inhibition of downstream gene expression. Recently, considerable studies have been made to determine the effects of DNA methylation on complex disease. However, further studies are necessary to find the multiple interactions of many DNA methylation sites and their association with cancer. Here, to assess DNA methylation modules potentially relevant to disease, we use an Estimation of Distribution Algorithm (EDA) to identify high-order interaction of DNA methylated sites (or modules) that are potentially relevant to disease. The method builds a probabilistic dependency model to produce a solution that is a set of discriminative methylation sites. The algorithm is applied to array- and sequencing-based high-throughput DNA methylation profiling datasets. The experimental results show that it is able to identify DNA methylation modules for cancer.

Motivation Single-individual haplotyping (SIH) is critical in genomic association studies and genetic diseases analysis. However, most genomic analysis studies do not perform haplotype-phasing analysis due to its complexity. Several computational methods have been developed to solve the SIH problem, but these approaches have not generated sufficiently reliable haplotypes. Results Here, we propose a novel SIH algorithm, called PEATH (Probabilistic Evolutionary Algorithm with Toggling for Haplotyping), to achieve more accurate and reliable haplotyping. The proposed PEATH method was compared to the most recent algorithms in terms of the phased length, N50 length, switch error rate and minimum error correction. The PEATH algorithm consistently provides the best phase and N50 lengths, as long as possible, given datasets. In addition, verification of the simulation data demonstrated that the PEATH method outperforms other methods on high noisy data. Additionally, the experimental results of a real dataset confirmed that the PEATH method achieved comparable or better accuracy. Availability and implementation Source code of PEATH is available at https://github.com/jcna99/PEATH. Supplementary information Supplementary data are available at Bioinformatics online.

Surgical archives of tumor specimens are often impure. The presence of RNA transcripts from nontumor cells, such as immune and stromal cells, can impede analyses of cancer expression profiles. To systematically analyze the impact of tumor purity, the gene expression profiles and tumor purities were obtained for 7,794 tumor specimens across 21 tumor types (available in The Cancer Genome Atlas consortium). First, we observed that genes with roles in immunity and oxidative phosphorylation were significantly inversely correlated and correlated with the tumor purity, respectively. The expression of genes implicated in immunotherapy and specific immune cell genes, along with the abundance of immune cell infiltrates, was substantially inversely correlated with tumor purity. This relationship may explain the correlation between immune gene expression and mutation burden, highlighting the need to account for tumor purity in the evaluation of expression markers obtained from bulk tumor transcriptome data. Second, examination of cluster membership of gene pairs, with or without controlling for tumor purity, revealed that tumor purity may have a substantial impact on gene clustering across tumor types. Third, feature genes for molecular taxonomy were analyzed for correlation with tumor purity, and for some tumor types, feature genes representing the mesenchymal and classical subtypes were inversely correlated and correlated with tumor purity, respectively. Our findings indicate that tumor purity is an important confounder in evaluating the correlation between gene expression and clinicopathologic features such as mutation burden, as well as gene clustering and molecular taxonomy. Cancer Immunol Res; 6(1); 87–97. ©2017 AACR.

// Sung Hak Lee 1, * , Seung Hyun Jung 4, 6, * , Tae-Min Kim 2 , Je-Keun Rhee 2 , Hyeon-Chun Park 3, 4 , Min Sung Kim 5, 6 , Sung Soo Kim 7 , Chang Hyeok An 8 , Sug Hyung Lee 5, 6 , Yeun-Jun Chung 3, 4, 5 1 Departments of Hospital Pathology, The Catholic University of Korea, Seoul, Korea 2 Departments of Medical Informatics, The Catholic University of Korea, Seoul, Korea 3 Departments of Microbiology, The Catholic University of Korea, Seoul, Korea 4 Departments of Integrated Research Center for Genome Polymorphism, The Catholic University of Korea, Seoul, Korea 5 Departments of Pathology, The Catholic University of Korea, Seoul, Korea 6 Departments of Cancer Evolution Research Center, The Catholic University of Korea, Seoul, Korea 7 Department of Internal Medicine, The Catholic University of Korea, Seoul, Korea 8 Departments of General Surgery The Catholic University of Korea, Seoul, Korea * These two authors contributed equally to this work Correspondence to: Yeun-Jun Chung, email: yejun@catholic.ac.kr Sug Hyung Lee, email: suhulee@catholic.ac.kr Chang Hyeok An, email: achcolo@catholic.ac.kr Keywords: colon, adenoma, high grade adenoma, mutation, whole-exome sequencing Received: November 14, 2016 Accepted: December 02, 2016 Published: December 25, 2016 ABSTRACT Although gene-to-gene analyses identified genetic alterations such as APC, KRAS and TP53 mutations in colon adenomas, it is largely unknown whether there are any others in them. Mutational profiling of high-grade colon adenoma (HGCA) that just precedes colon carcinoma might identify not only novel adenoma-specific genes but also critical genes for its progression to carcinoma. For this, we performed whole-exome sequencing (WES) of 12 HGCAs and identified 11 non-hypermutated and one hypermutated ( POLE -mutated) cases. We identified 22 genes including APC, KRAS, TP53, GNAS, NRAS, SMAD4, ARID2 , and PIK3CA with non-silent mutations in the cancer Census Genes. Bi-allelic and mono-allelic APC alterations were found in nine and one HGCAs, respectively, while the other two harbored wild-type APC . Five HGCAs harbored either mono-allelic (four HGCAs) or bi-allelic (one HGCA) SMAD4 mutation or 18q loss that had been known as early carcinoma-specific changes. We identified MTOR , ACVR1B , GNAQ , ATM , CNOT1 , EP300 , ARID2, RET and MAP2K4 mutations for the first time in colon adenomas. Our WES data is largely matched with the earlier ‘adenoma-carcinoma model’ ( APC, KRAS , NRAS and GNAS mutations), but there are newly identified SMAD4 , MTOR , ACVR1B , GNAQ , ATM , CNOT1 , EP300 , ARID2, RET and MAP2K4 mutations in this study. Our findings provide resource for understanding colon premalignant lesions and for identifying genomic clues for differential diagnosis and therapy options for colon adenomas and carcinomas.

Background and aims Internet gaming disorder (IGD) has gained recognition as a potential new diagnosis in the fifth revision of the Diagnostic and Statistical Manual of Mental Disorders, but genetic evidence supporting this disorder remains scarce. Methods In this study, targeted exome sequencing was conducted in 30 IGD patients and 30 control subjects with a focus on genes linked to various neurotransmitters associated with substance and non-substance addictions, depression, and attention deficit hyperactivity disorder. Results rs2229910 of neurotrophic tyrosine kinase receptor, type 3 (NTRK3) was the only single nucleotide polymorphism (SNP) that exhibited a significantly different minor allele frequency in IGD subjects compared to controls (p = .01932), suggesting that this SNP has a protective effect against IGD (odds ratio = 0.1541). The presence of this potentially protective allele was also associated with less time spent on Internet gaming and lower scores on the Young's Internet Addiction Test and Korean Internet Addiction Proneness Scale for Adults. Conclusions The results of this first targeted exome sequencing study of IGD subjects indicate that rs2229910 of NTRK3 is a genetic variant that is significantly related to IGD. These findings may have significant implications for future research investigating the genetics of IGD and other behavioral addictions.

Intraindividual tumoural heterogeneity (ITH) is a hallmark of solid tumours and impedes accurate genomic diagnosis and selection of proper therapy. The aim of this study was to identify ITH of ovarian high-grade serous carcinomas (OSCs) and to determine the utility of ascitic cancer cells as a resource for mutation profiling in spite of ITH. We performed whole-exome sequencing, copy number profiling and DNA methylation profiling of four OSC genomes by using multiregional biopsies from 13 intraovarian lesions, 12 extraovarian tumour lesions (omentum/peritoneum), and ascitic cells. We observed substantial levels of heterogeneity in mutations and copy number alterations (CNAs) of the OSCs. We categorized the mutations into 'common', 'shared' and 'private' according to the regional distribution. Six common, eight shared and 24 private mutations were observed in known cancer-related genes. Common mutations had a higher mutant allele frequency, and included TP53 mutations in all four OSCs. Region-specific chromosomal amplifications and deletions involving BRCA1, PIK3CA and RB1 were also identified. It is of note that the mutations detected in ascitic cancer cells represented 92.3-100% of overall somatic mutations in the given case. Phylogenetic analyses of ascitic genomes predicted a polyseeding origin of somatic mutations in ascitic cells. Our results demonstrate that, despite ITH, somatic mutations, CNAs and DNA methylations in both 'common' category and cancer-related genes were highly conserved in ascitic cells of OSCs, highlighting the clinical relevance of genome analysis of ascitic cells. Ascitic tumour cells may serve as a potential resource for discovering somatic mutations of primary OSC with diagnostic and therapeutic relevance. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Pheochromocytoma and paraganglioma (PPGL) are tumours that arise from chromaffin cells. Some genetic mutations influence PPGL, among which, those in genes encoding subunits of succinate dehydrogenase (SDHA, SDHB, SDHC and SDHD) and assembly factor (SDHAF2) are the most relevant. However, the risk of metastasis posed by these mutations is not reported except for SDHB and SDHD mutations. This study aimed to update the metastatic risks, considering prevalence and incidence of each SDHx mutation, which were dealt formerly all together.We searched EMBASE and MEDLINE and selected 27 articles. The patients included in the studies were divided into three groups depending on the presence of PPGL. We checked the heterogeneity between studies and performed a meta-analysis using Hartung-Knapp-Sidik-Jonkman method based on a random effect model.The highest PPGL prevalence was for SDHB mutation, ranging from 23% to 31%, and for SDHC mutation (23%), followed by that for SDHA mutation (16%). The lowest prevalence was for SDHD mutation, ranging from 6% to 8%. SDHAF2 mutation showed no metastatic events. The PPGL incidence showed a tendency similar to that of its prevalence with the highest risk of metastasis posed by SDHB mutation (12%-41%) and the lowest risk by SDHD mutation (~4%).There was no integrated evidence of how SDHx mutations are related to metastatic PPGL. However, these findings suggest that SDHA, SDHB and SDHC mutations are highly associated and should be tested as indicators of metastasis in patients with PPGL.

// Jaeyun Jung 1, * , Cue Hyunkyu Lee 3, * , Hyang Sook Seol 4 , Yeon Sook Choi 4 , Eunji Kim 3, 5 , Eun Ji Lee 1 , Je-Keun Rhee 6 , Shree Ram Singh 7 , Eun Sung Jun 1 , Buhm Han 3 , Seung Mo Hong 8 , Song Cheol Kim 9 , Suhwan Chang 1, 2 1 Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul, Korea 2 Department of Physiology, University of Ulsan College of Medicine, Seoul, Korea 3 Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea 4 Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea 5 Department of Chemistry, Seoul National University, Seoul, Korea 6 Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea 7 Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA 8 Department of Pathology, Asan Medical Center, Seoul, Korea 9 Department of Surgery, Asan Medical Center, Seoul, Korea * These authors have contributed equally to this work Correspondence to: Song Cheol Kim, email: drksc@amc.seoul.kr Suhwan Chang, email: suhwan.chang@amc.seoul.kr Keywords: pancreatic cancer, patient-derived xenograft, single nucleotide polymorphism, cancer panel, heterogeneity Received: June 01, 2016 Accepted: August 08, 2016 Published: August 23, 2016 ABSTRACT Pancreatic ductal adenocarcinoma (PDAC) is the most challenging type of cancer to treat, with a 5-year survival rate of

Mutation profiles and intratumoral heterogeneity are not well understood for benign gastric adenomas, some of which progress into malignant gastric adenocarcinomas. In this study, we performed whole-exome sequencing of three microsatellite stable (MSS) and two microsatellite instability-high (MSI-H) gastric adenomas with three regional tumor biopsies per case. We observed that the mutation abundance of benign gastric adenomas was comparable to those of gastric adenocarcinomas, suggesting that the mutational makeup for gastric carcinogenesis may already be achieved in benign adenomas. The extent of intratumoral heterogeneity was more substantial for MSS genomes in that only 1% - 14% of somatic mutations were common across the regional biopsies or ‘public’, while 50% - 94% of mutations were public in MSI-H gastric adenomas. We observed biallelic, loss-of-functional events of APC with truncating mutations and/or 5q losses for all cases, mostly as public events. All MSS gastric adenomas also harbored ARID2 truncating mutations, often as multiple, region-specific ones indicative of convergent evolution. Hotspot missense mutations on known cancer genes such as ERBB2 and KRAS were largely observed as region-specific aberrations. These findings suggest that biallelic functional APC inactivation initiates the gastric carcinogenesis and is followed by mutations of histone modifiers and then activation of known cancer-related genes. As the first exome-wide multi-region mutational profiling of gastric adenomas, our study provides clues on the chronological sequence of somatic mutations and their clonal architectures in early gastric carcinogenesis.

Tumor-initiating cells are important for the formation and maintenance of tumor bulks in various tumors. To identify surface markers of liver tumor-initiating cells, we performed primary tumorsphere culture and analyzed the expression of cluster of differentiation (CD) antigen genes using NanoString. Interestingly, we found significant upregulation of the complement proteins (p = 1.60 × 10(-18)), including C7 and CFH. Further studies revealed that C7 and CFH are required to maintain stemness in liver cancer cells. Knockdown of C7 and CFH expression abrogated tumorsphere formation and induced differentiation, whereas overexpression stimulated stemness factor expression as well as in vivo cell growth. Mechanistically, by studying C7 and CFH-dependent LSF-1 expression and its direct role on stemness factor transcription, we found that LSF-1 is involved in this regulation. Taken together, our data demonstrate the unprecedented role of complement proteins on the maintenance of stemness in liver tumor-initiating cells.

Genome-wide association studies have expanded our understanding of the relationship between the human genome and disease. However, because of current technical limitations, it is still challenging to clearly resolve diploid sequences, that is, two copies for each chromosome. One copy of each chromosome is inherited from each parent and the genomic function is determined by the interplay between the alleles represented as genotypes in the diploid sequences. Thus, to understand the nature of genetic variation in biological processes, including disease, it is necessary to determine the complete genomic sequence of each haplotype. Although there are experimental approaches for haplotype sequencing that physically separate the chromosomes, these methods are expensive and laborious and require special equipment. Here, we review the computational approaches that can be used to determine the haplotype phase. Since 1990, many researchers have tried to reconstruct the haplotype phase using a variety of computational methods, and some researches have been successfully help to determine the haplotype phase. In this review, we investigate how the computational haplotype determination methods have been developed, and we present the remaining problems affecting the determination of the haplotype of single individual using next-generation sequencing methods.

Pancreatic cancer is one of the most difficult cancers to cure due to the lack of early diagnostic tools and effective therapeutic agents. In this study, we aimed to isolate new bioactive compounds that effectively kill pancreatic ductal adenocarcinoma (PDAC) cells, but not untransformed, human pancreatic ductal epithelial (HPDE) cells. To this end, we established four primary PDAC cell lines and screened 4141 compounds from four bioactive‐compound libraries. Initial screening yielded 113 primary hit compounds that caused over a 50% viability reduction in all tested PDAC cells. Subsequent triplicate, dose‐dependent analysis revealed three compounds with a tumor cell‐specific cytotoxic effect. We found that these three compounds fall into a single category of thiopurine biogenesis. Among them, 6‐thioguanine (6‐TG) showed an IC50 of 0.39–1.13 μm toward PDAC cells but had no effect on HPDE cells. We propose that this cancer selectivity is due to differences in thiopurine methyltransferase (TPMT) expression between normal and cancer cells. This enzyme is responsible for methylation of thiopurine, which reduces its cytotoxicity. We found that TPMT levels were lower in all four PDAC cell lines than in HPDE or Panc1 cells, and that knockdown of TPMT in HPDE or Panc1 cells sensitized them to 6‐TG. Lastly, we used a patient‐derived xenograft model to confirm that 6‐TG has a significant antitumor effect in combination with gemcitabine. Overall, our study presents 6‐TG as a strong candidate for use as a therapeutic agent against PDAC with low levels of TPMT.

Figure S2. Hotspot mutation and CCF. (a) Maximum value of CCF in COADREAD. Each dot means a tumor patient with a somatic mutation in APC hotspots. (b) Distribution of CCFs for APC and TP53 mutations in COADREAD. The figure is plotted for the patients with APC:Q1387 hotspot mutation. Red is CCF distribution for APC and blue is for TP53. (PDF 35 kb)

Figure S1. Determination of the minimum sample size for the experiments (a) 5 percentile of the 100,000 random re-sampling experiments with the number of cases (b) Distribution of the frequency for the mutant gene pairs. (PDF 23 kb)