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Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary.Significance:Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

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a. Age at the time of diagnosis of Hispanic/Latino (Hs/L) patients from this study, and of Asian and White patients analyzed by The Cancer Genome Atlas (TCGA). Horizontal lines, medians; boxes, interquartile ranges; whiskers, maximum and minimum values. P < 0.001. b. Second view of the principal component analysis of whole-exome sequencing data, as analyzed by Locating Ancestry from SEquence Reads to define patient ancestry of Asian and White patients analyzed by the TCGA and Hispanic/Latino patients from this study, as compared to reference from the Human Genome Diversity Project (HGDP).

Supplementary Methods

a. Chromatograms confirming germline CDH1 mutations identified on whole-exome sequencing. b. Age of patients who are wild-type for CDH1 (blue) or carry germline CDH1 mutations (red). Bar denotes median.

Subgroup analysis of overall survival for the Hispanic/Latino cohort. Kaplan-Meier curves comparing: a. all patients by individual mRNA clusters, P < 0.01, b. patients with genomically stable tumors, P < 0.05, c. patients with chromosomal instable tumors, P < 0.05, d. patients with diffuse-type tumors, P < 0.05, e. patients with intestinal-type tumors, P < 0.05.

a. The Cancer Genome Atlas algorithm to categorize gastric cancer into four molecular subtypes: Epstein-Barr virus infected (EBV, red), microsatellite instability (MSI, blue), chromosomal instability (CIN, purple), and genomically stable (GS, green). SCNA = somatic copy number alterations. b. MSIsensor score. Whole-exome sequence data from each Hispanic/Latino cancer sample were analyzed with MSIsenor. Gray bar denotes total mutation burden per megabase. Yellow bar denotes calculated MSIsensor score. Samples with score {greater than or equal to} 10 were considered to be microsatellite unstable.

a. Immunohistochemistry for E-cadherin in six patients who have germline CDH1 mutations. Red long arrows denote cancer cells, short green arrows denote normal stomach glands. P: patient. For P16, normal stomach and cancer cells are shown in two separate panels. Scale bar = 50 μm. b. Amount of DNA in which the CDH1 promoter is methylated.

Immunofluorescence staining for E-cadherin in Chinese hamster ovary cells overexpressing wild-type (WT) CDH1, A286G, or G1849A variants, all of which have wild-type membranous localization. Scale bar = 10 μm.

BackgroundDrug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study.MethodsIn this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available athttps://github.com/compbiolabucf/drug-sensitivity-prediction.ResultsIn the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug’s mechanism of action.ConclusionsNetwork-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary. Significance: Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

Simple Summary Currently, the optimal treatment for colorectal cancer (CRC) is planned on the basis of the results of preoperative imaging studies. Previous studies investigating the impact of radiomics signatures derived from positron-emission tomography (PET) images mainly focused on patients with rectal cancer, who underwent preoperative chemoradiotherapy, and included a relatively small number of patients, without a validation set. The impact of PET-based radiomics signature analysis in patients undergoing curative-intent radical surgery, with or without chemotherapy, has not been extensively investigated. Thus, we aimed to identify the prognostic value of radiomics signature from18F-fluorodeoxyglucose (18F-FDG) PET images by assessing the imaging features to predict the progression-free survival in patients with CRC. This study demonstrated that radiomics features derived from PET-CT images can help stratify patient prognosis and additionally increase diagnostic accuracy with respect to conventional clinicopathological data-driven prediction model in patients with CRC. Abstract The aim of this study was to investigate the prognostic value of radiomics signatures derived from 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent 18F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on 18F-FDG-PET. Patients were divided into a training and validation set by random sampling. A least absolute shrinkage and selection operator Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and its clinical utility was assessed in the validation set. A total of 381 patients with surgically resected CRC patients (training set: 228 vs. validation set: 153) were included. In the training set, a radiomics signature labeled as a rad_score was generated using two PET-derived features, such as gray-level run length matrix long-run emphasis (GLRLM_LRE) and gray-level zone length matrix short-zone low-gray-level emphasis (GLZLM_SZLGE). Patients with a high rad_score in the training and validation set had a shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Textural features derived from 18F-FDG-PET images may enable detailed stratification of prognosis in patients with CRC.

Additional file 1: Figure S1 and Table S1.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared to patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared to data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically-stable subtype tumors compared to Asian and White patients (65% vs 21% vs 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline mutations in CDH1. Mutation carriers were significantly younger than non-carriers (41 vs 50 years, P < 0.05). In silico algorithms predicted 5 variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino gastric cancer patients possess unique genomic landscapes, including a high rate of CDH1 germline mutations that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes as compared to patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients were included in previous studies. We performed a large, integrated genomic analysis of gastric cancer samples from Hispanic/Latino patients. Whole-exome sequencing (WES) and RNA sequencing were performed on 57 Hispanic/Latino gastric cancer patient samples. Germline analysis was conducted on 83 patients. Functional testing of CDH1 germline mutations was performed in Chinese hamster ovary cells. Tumors from Hispanic/Latino patients were significantly enriched for the genomically-stable subtype (as defined by The Cancer Genome Atlas), compared to Asians and Whites (65% vs 21% vs 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type gastric cancer, 7 (16%) had germline mutations in CDH1. Mutation carriers were significantly younger than non-carriers (41 vs 50 years, P < 0.05). E-cadherin expression was reduced in 5 of 6 mutation carrier tumor samples available for analysis. In silico algorithms predicted 5 variants were deleterious. For the two variants that were predicted to be benign, we demonstrated that the mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino gastric cancer patients possess unique genomic landscapes. This includes a high rate of CDH1 germline mutations that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary.

The introduction of insertion-deletions (INDELs) by non-homologous end-joining (NHEJ) pathway underlies the mechanistic basis of CRISPR-Cas9-directed genome editing. Selective gene ablation using CRISPR-Cas9 is achieved by installation of a premature termination codon (PTC) from a frameshift-inducing INDEL that elicits nonsense-mediated decay (NMD) of the mutant mRNA. Here, by examining the mRNA and protein products of CRISPR targeted genes in a cell line panel with presumed gene knockouts, we detect the production of foreign mRNAs or proteins in ~50% of the cell lines. We demonstrate that these aberrant protein products stem from the introduction of INDELs that promote internal ribosomal entry, convert pseudo-mRNAs (alternatively spliced mRNAs with a PTC) into protein encoding molecules, or induce exon skipping by disruption of exon splicing enhancers (ESEs). Our results reveal challenges to manipulating gene expression outcomes using INDEL-based mutagenesis and strategies useful in mitigating their impact on intended genome-editing outcomes., CRISPR-Cas9 genome editing is presumed to knock out gene function by generating a frameshift during NHEJ repair. Here, the authors investigate mRNA and protein expression in edited lines and find genome editing can generate internal ribosome entry sites or alternatively spliced variants.

Hispanics/Latinos in the U.S. have higher incidence and mortality from gastric cancer as compared to non-Hispanic Whites. However, few Hispanic/Latino patients have been included in basic science or clinical studies in gastric cancer We analyzed information from the National Cancer Database and found that compared to Asian and White patients, Hispanics/Latino patients with gastroesophageal junction (GEJ) or gastric adenocarcinomas had a higher proportion of Stage 3 or 4 disease (64% of Hispanic/Latino patients, 58% of White patients and 54% of Asians (P < 0.001)) and were younger at the time of diagnosis (median age in years (interquartile range): Hispanic/Latino 62 (50-73), Asian: 66 (56-76), and White: 68 (59-77), P < 0.001). To determine if there were genomic factors associated with these clinical outcomes, we performed whole-exome and RNA sequencing on GEJ and GC samples from 36 Hispanic/Latino patients treated in North Texas. Using the Locating Ancestry from SEquence Reads algorithm, we analyzed the sequencing data from our cohort and the publicly available information of White and Asian patients sequenced by The Cancer Genome Atlas (TCGA) to determine ancestry. We found that each group bundled within distinct clusters. Next, we classified the tumors based on the molecular categorization scheme established by the TCGA, which defines four groups using a step-wise algorithm. Tumors are first categorized by Epstein-Barr virus (EBV) infection status and then by microsatellite instability (MSI). The remaining samples undergo somatic copy number alteration (SCNA) analysis, with high rates defining the chromosomal instability (CIN) group and low rates designating the genomically stable (GS) tumors. In our cohort of Hispanic/Latino patients, we found no EBV, two MSI (6%), ten CIN (28%), and 24 GS (66%) subtype tumors. This is a much a higher proportion of GS subtype tumors as compared to White and Asian patients sequenced by the TCGA (P < 0.001). In addition, we found a relatively high rate of germline CDH1 mutations, which are known to cause diffuse-type gastric cancer, that may explain the younger presentation age of Hispanic/Latino patients. Finally, we identified a molecular signature related to activated immune response that was prognostic for overall survival in a subset of patients. Our findings have significant clinical implications in terms of screening, genetic counseling, and treatment for Hispanic/Latino patients. Citation Format: Sam C. Wang, Yunku Yeu, Suntrea T.G. Hammer, Min Zhu, Ibrahim Nassour, Jeanne Shen, Deepak Agarwal, Scott I. Reznick, John C. Mansour, Adam C. Yopp, Hao Zhu, Tae Hyun Hwang, Matthew R. Porembka. Molecular subtyping of gastroesophageal junction and gastric adenocarcinomas from American Hispanic/Latino patients [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr A107.

MotivationRecent studies showed that a phenotype-driven analysis of whole exome sequencing (WES) could provide more accurate and clinically relevant genetic variants.ResultsWe develop a computational tool called Divine that integrates patients’ phenotype(s) and WES data with 30 prior biological knowledge (e.g., human phenotype ontology, gene ontology, pathway database, protein-protein interaction networks, pathogenicity by the amino acid change due to polymorphism, and hot-spot protein domains) to prioritize potential disease-causing genes. In a retrospective study with 22 real and four simulated data set, Divine ranks the same pathogenic genes confirmed by the original studies 5th on average out of a thousand of mutated genes and outperforms existing state-of-the-art methods.Availabilityhttps://github.com/hwanglab/divineContacthwangt@ccf.orgSupplementary informationSupplementary Document is attached at the end of the page.

BACKGROUND: Wilms tumor and rhabdoid tumor can have similar clinical presentations, but they have distinct histological and biological features. For instance, Wilms tumors commonly bear mutations in kidney differentiation or microRNA processing genes, whereas rhabdoid tumor is characterized by loss of SMARCB1. AIMS: We initially set out to characterize and identify tumor suppressor microRNAs in WT‐CLS1, which had been described as a Wilms tumor cell line. METHODS AND RESULTS: We characterized the cell line WT‐CLS1 by whole exome sequencing, RNA‐seq, and xenograft histology. We measured the effect of microRNA overexpression on WiT49, WT‐CLS1, BT‐12, and CHLA‐06‐ATRT. We found that miR‐16 significantly impairs cell proliferation in WT‐CLS1 by repressing numerous cell cycle genes, including the D‐type cyclins. In addition, we found that the WT‐CLS1 cell line demonstrates the classic histological, mutational, and transcriptional hallmarks of rhabdoid tumor, including SMARCB1 loss. Lastly, miR‐16 also represses cell cycle genes and impairs proliferation in the BT‐12 and CHLA‐06‐ATRT rhabdoid tumor cell lines. CONCLUSIONS: The loss of SMARCB1 warrants reclassification of WT‐CLS1 as rhabdoid tumor. Overexpression of miR‐16 significantly abrogates proliferation of WT‐CLS1 and other rhabdoid tumor cell lines. Further studies are necessary to gain insight into the potential for miR‐16 to be a tumor suppressor or a novel therapeutic in rhabdoid tumor.

Identifying alternative indications for known drugs is important for the pharmaceutical industry. Many computational methods have been proposed for predicting unknown associations between drugs and target proteins associated with diseases. To produce better prediction, researchers should not only develop accurate algorithms but identify good features that reflect intracellular systems. In this paper, we proposed a novel method for exploiting protein localization. We generated localization vectors (LVs) from protein localization and propagated LVs through a protein interaction network to increase the coverage of the localization information. The LVs showed distinct patterns among targets of known drugs as well as independent characteristics compared to existing features. Based on the experimental results, we determined that including LVs improves cross-validation accuracy and, produces better novel predictions with real and independent clinical trial data. Moreover, the propagation of LVs showed a positive result that it can help in increasing the coverage of the prediction results.

BackgroundRhabdoid tumor is a highly aggressive pediatric cancer characterized by biallelic loss and/or mutation of SMARCB1. Outcomes remain poor, and there are no established ways to target the tumorigenic pathways driven by SMARCB1 inactivation. SMARCB1 loss leads to an increase in cyclin D transcription.ProcedureWe characterized the cell line WT-CLS1, which has been described previously as Wilms tumor, by whole-exome sequencing, RNA-seq, and xenograft histology. We measured the effect of microRNA overexpression on WT-CLS1, BT-12, and CHLA-06-ATRT.ResultsWe found that WT-CLS1 demonstrates the histological, mutational, and transcriptional hallmarks of rhabdoid tumor. Because the microRNAs let-7 and miR-16 can target cyclin D genes, we next overexpressed each of these microRNAs in WT-CLS1. We found that miR-16 reduced cell accumulation. This was accompanied by a decrease in proliferation markers and an increase in apoptosis markers. These results were replicated in the BT-12 and CHLA-06-ATRT cell lines.ConclusionsThe loss-of-function SMARCB1 mutation found in WT-CLS1, in conjunction with immunohistochemical and gene expression analysis, warrants reclassification of this cell line as rhabdoid tumor. Proliferation of WT-CLS1 and other rhabdoid tumor cell lines is significantly abrogated by miR-16 overexpression. Further studies are necessary to gain insight into the potential for miR-16 to be used as a novel therapeutic in rhabdoid tumor.Abbreviations(ATRT)atypical teratoid/rhabdoid tumor(miRNA)microRNA(dox)doxycycline(OD595)optical density at 595 nm(CDK4)cyclin-dependent kinase 4

Glioblastoma is the most prevalent and lethal brain tumor. Within glioblastoma, stem-like glioma stem cells (GSCs) contribute to the heterogeneous and highly aggressive nature of the tumor. It is critical to decipher the changes in the regulation of gene expression that accompany the differentiation from GSCs to differentiated glioma cells (DGCs). To date, such changes have only been investigated on the transcriptional and proteomic levels but not on the translational level, despite the key role that the regulation of protein production plays in all aspects of cellular function. Here, we interrogated changes in the translational landscape during the differentiation of patient-derived GSCs into DGCs. We utilized ribosome profiling to assess translation on a transcriptome wide level in both GSCs and DGCs. We identified a translational repression program in GSCs wherein a significant subset of transcripts (>1,600) is translationally repressed. Roughly 80% of these transcripts are de-repressed upon differentiation in DGCs, including genes with roles in Wnt-signaling and ribosome structure and function. Most notably, we find that the poly(A) binding protein interacting protein 2B (PAIP2B) is most preferentially translated gene in GSCs when compared to DGCs. PAIP2B is a broad yet transcript-specific repressor of translation that antagonizes poly(A) binding proteins (PABPs), which are broad activators of translation. Interestingly, PABPs are preferentially translated in DGCs. Our results suggest that glioblastoma cells utilize the PAIP2B-PABP axis to exert translational control over the transcriptome in order to maintain the cancer stem cell state and to drive the differentiation of GSCs.

The biclustering method is a useful co-clustering technique to identify biologically relevant gene modules. In this paper, we propose a novel method to find not only functionally-related gene modules but also state specific gene modules by applying a genetic algorithm to gene expression data. To identify these gene modules, the proposed method finds biclusters in which genes are statistically overexpressed or under expressed, and are differentially-expressed in the samples in the bicluster compared to the samples not in the bicluster. In addition, we improve the genetic algorithm by adding a selection pool for preserving the diversity of the population. The resulting gene modules exhibit better performances than comparative methods in the GO (Gene Ontology) term enrichment test and an analysis connection between gene modules and disease. This is especially the case with gene modules that receive the highest score in the breast cancer dataset; they are closely linked to the ribosome pathway. Recent studies show that dysregulation of ribosome biogenesis is associated with breast tumor progression.

Gene clustering is a method for finding gene sets which are related to the same biological processes or molecular function. In order to find these gene sets, previous studies have clustered genes which showed similar mRNA expression or a specific expression pattern in a (sub) sample set. However, for two contrasting groups of samples, it is not easy to identify gene sets which show significant expression pattern in only one group using current gene clustering methods. Existing biclustering methods use only one group (disease) of samples. It is hard to identify disease specific biclusters which are differentially expressed in the disease although those methods can find biclusters which have specific expression pattern. Here, we proposed a novel method using a genetic algorithm in gene expression data, in order to find gene sets which can represent specific subtype of cancer. Proposed method finds gene sets which have statistically differential mRNA expression on two contrasting samples and fraction of cancer samples. The resulting gene modules share higher number of GO (Gene Ontology) terms related to a specific disease than gene modules identified by current algorithms. We also identify that when we integrate protein-protein interaction data with gene expression data of colorectal cancer samples, proposed method can find more functionally related gene sets.

Background Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable. Methods For more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other. Results We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection. Conclusions Our algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.