Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Mucosal melanomas of different anatomic sites share a common global DNA methylation profile with cutaneous melanoma but show location-dependent patterns of genetic and epigenetic alterations

Cutaneous, ocular, and mucosal melanomas are histologically indistinguishable tumors that are driven by a different spectrum of genetic alterations. With current methods, identification of the site of origin of a melanoma metastasis is challenging. DNA methylation profiling has shown promise for the identification of the site of tumor origin in various settings. Here we explore the DNA methylation landscape of melanomas from different sites and analyze if different melanoma origins can be distinguished by their epigenetic profile. We performed DNA methylation analysis, next generation DNA panel sequencing, and copy number analysis of 82 non-cutaneous and 25 cutaneous melanoma samples. We further analyzed eight normal melanocyte cell culture preparations. DNA methylation analysis separated uveal melanomas from melanomas of other primary sites. Mucosal, conjunctival, and cutaneous melanomas shared a common global DNA methylation profile. Still, we observed location-dependent DNA methylation differences in cancer-related genes, such as low frequencies of RARB (7/63) and CDKN2A promoter methylation (6/63) in mucosal melanomas, or a high frequency of APC promoter methylation in conjunctival melanomas (6/9). Furthermore, all investigated melanomas of the paranasal sinus showed loss of PTEN expression (9/9), mainly caused by promoter methylation. This was less frequently seen in melanomas of other sites (24/98). Copy number analysis revealed recurrent amplifications in mucosal melanomas, including chromosomes 4q, 5p, 11q and 12q. Most melanomas of the oral cavity showed gains of chromosome 5p with TERT amplification (8/10), while 11q amplifications were enriched in melanomas of the nasal cavity (7/16). In summary, mucosal, conjunctival, and cutaneous melanomas show a surprisingly similar global DNA methylation profile and identification of the site of origin by DNA methylation testing is likely not feasible. Still, our study demonstrates tumor location-dependent differences of promoter methylation frequencies in specific cancer-related genes together with tumor site-specific enrichment for specific chromosomal changes and genetic mutations. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Sex Differences in Cancer Genomes: Much Learned, More Unknown

Cancer is a leading cause of death worldwide. Sex influences cancer in a bewildering variety of ways. In some cancer types, it affects prevalence; in others, genomic profiles, response to treatment, or mortality. In some, sex seems to have little or no influence. How and when sex influences cancer initiation and progression remain a critical gap in our understanding of cancer, with direct relevance to precision medicine. Here, we note several factors that complicate our understanding of sex differences: representativeness of large cohorts, confounding with features such as ancestry, age, obesity, and variability in clinical presentation. We summarize the key resources available to study molecular sex differences and suggest some likely directions for improving our understanding of how patient sex influences cancer behavior.

Construction of a Pearson- and MIC-Based Co-expression Network to Identify Potential Cancer Genes

The weighted gene co-expression network analysis (WGCNA) method constructs co-expressed gene modules based on the linear similarity between paired gene expressions. Linear correlations are the main form of similarity between genes, however, nonlinear correlations still existed and had always been ignored. We proposed a modified network analysis method, WGCNA-P + M, which combines Pearson's correlation coefficient and the maximum information coefficient (MIC) as the similarity measures to assess the linear and nonlinear correlations between genes, respectively. Taking two real datasets, GSE44861 and liver hepatocellular carcinoma (TCGA-LIHC), as examples, we compared the gene modules constructed by WGCNA-P + M and WGCNA from four perspectives: the "Usefulness" score, GO enrichment analysis on genes in the gray module, prediction performance of the top hub gene, survival analysis and literature reports on different hub genes. The results showed that the modules obtained by WGCNA-P + M are more biological meaningful, the hub genes obtained from WGCNA-P + M have more potential cancer genes.

Identification of key genes in osteosarcoma - before and after CDK7 treatment

BACKGROUND

Osteosarcoma is one of the most common bone tumors, with a high degree of malignancy and a poor prognosis. Recent studies have shown that THZ2, a cyclin-dependent kinase 7 inhibitor, can exhibit strong antibone tumor effects in vivo and in vitro by inhibiting transcriptional activity. In this study, by screening the differentially expressed genes (DEGs) of osteosarcoma cells before and after THZ2 treatment, it provides new possible targets for the future targeted therapy of osteosarcoma.

METHODS

Download the gene expression profile of GSE134603 from the Gene Expression Omnibus database, and use the R software package "limma Geoquery" to screen DEGs. DAVID database was used for gene ontology analysis of DEGs. Use search tool for the retrieval of interacting genes online database and Cytoscape software to construct protein-protein interaction network. Use the "MCODE" plugin in Cytoscape to analyze key molecular complexes (module) of DEGs, and use the "Cluego" plugin to perform Kyoto Encyclopedia of Genes and Genomes enrichment analysis on module genes. The Hub gene is selected from the genes in DEGs that coexist in the top 30 Degree and the Kyoto Encyclopedia of Genes and Genomes pathway.

RESULTS

A total of 1033 DEGs were screened, including 800 up-regulated genes and 233 down-regulated genes. Gene ontology analysis showed that cell component is the main enrichment area of DEGs, mainly in the nucleus, cytoplasm, and nucleoplasm. In addition, in molecular function analysis, DEGs are mainly enriched in the process of protein binding. In biological process analysis, changes in DEGs can also be observed in transcription and regulation using DNA as a template. Twenty-nine module genes are enriched in the Ribosome biogenesis in eukaryotes pathway. Finally, 4 key genes are drawn: essential for mitotic growth 1, U3 SnoRNP protein 3 homolog, U3 small nucleolar RNA-associated protein 15 homolog, and WD repeat domain 3.

CONCLUSION

This study found that the 4 genes essential for mitotic growth 1, U3 SnoRNP protein 3 homolog, U3 small nucleolar RNA-associated protein 15 homolog, WD repeat domain 3, and the ribosome biogenesis in eukaryotes pathway play a very important role in the occurrence and development of osteosarcoma, and can become a new target for molecular targeted therapy of osteosarcoma in the future.

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.

A Novel Cox Proportional Hazards Model for High-Dimensional Genomic Data in Cancer Prognosis

The Cox proportional hazards model is a popular method to study the connection between feature and survival time. Because of the high-dimensionality of genomic data, existing Cox models trained on any specific dataset often generalize poorly to other independent datasets. In this paper, we suggest a novel strategy for the Cox model. This strategy is included a new learning technique, self-paced learning (SPL), and a new gene selection method, SCAD-Net penalty. The SPL method is adopted to aid to build a more accurate prediction with its built-in mechanism of learning from easy samples first and adaptively learning from hard samples. The SCAD-Net penalty has fixed the problem of the SCAD method without an inherent mechanism to fuse the prior graphical information. We combined the SPL with the SCAD-Net penalty to the Cox model (SSNC). The simulation shows that the SSNC outperforms the benchmark in terms of prediction and gene selection. The analysis of a large-scale experiment across several cancer datasets shows that the SSNC method not only results in higher prediction accuracies but also identifies markers that satisfactory stability across another validation dataset. The demo code for the proposed method is provided in supplemental file.

In silico development and clinical validation of novel 8 gene signature based on lipid metabolism related genes in colon adenocarcinoma

BACKGROUND

Changes in lipid metabolism pathways play a major role in colon carcinogenesis and development. Hence, we conducted a systematic analysis of lipid metabolism-related genes to explore new markers that predict the prognosis of colon adenocarcinoma (COAD).

METHODS

The non-negative Matrix Factorization (NMF) algorithm was applied to identify the molecular subtypes based on lipid metabolism-related genes. A weighted correlation network analysis (WCGNA) was used to identify co-expressed genes, and Lasso multivariate Cox analysis was performed to build a risk prognosis model. A timer database was used to analyze the immune infiltration of the gene signature and the GSCALite database was used for genome-wide analysis of the gene signature.

RESULTS

TCGA-COAD samples were divided into 3 subtypes based on lipid metabolism-related genes. 2739 genes were identified by WGCNA analysis. Finally, an 8-gene signature (RTN2, FYN, HEYL, FAM69A, FBXL5, HMGN2, LGALS4, STOX1) was constructed that demonstrated good robustness in different datasets, as well as an independent risk factor for colon cancer patients' prognosis. In addition, our model's predictive efficacy overall was higher than that of the other published models, and the 8 genes' expression analysis indicated that RTN2, HEYL, and STOX1 were all expressed highly significantly in COAD, while FAM69A, FBXL5, LGALS4, FYN and HMGN2 were expressed significantly poorly in cancer tissues, which was confirmed in immunohistochemistry. The 8 genes were expressed significantly differently in COAD immune subtypes and correlated with clinical variables. Genome-wide analysis revealed that the STOX1 mutation frequency was the highest, and genome methylation influenced HEYL, FAM69A, and STOX1 gene expression significantly; further, the expression of HEYL and FBXL5 was correlated positively with Copy number variation (CNV) and was regulated significantly by CNV in most cancers. FBXL5 was correlated significantly with austocystin d and bafilomycin and played an important role in anti-tumor and immunotherapy. The HEYL, FYN, FAM69A, and RTN2 genes' expression was associated with the EMT pathway's activation, while LGALS4 and STOX1 were associated significantly with the EMT pathway's inhibition.

CONCLUSION

This study constructed an 8-gene signature as a novel marker to predict colon cancer patients' survival.

Translational adaptation of human viruses to the tissues they infect

Viruses need to hijack the translational machinery of the host cell for a productive infection to happen. However, given the dynamic landscape of tRNA pools among tissues, it is unclear whether different viruses infecting different tissues have adapted their codon usage toward their tropism. Here, we collect the coding sequences of 502 human-infecting viruses and determine that tropism explains changes in codon usage. Using the tRNA abundances across 23 human tissues from The Cancer Genome Atlas (TCGA), we build an in silico model of translational efficiency that validates the correspondence of the viral codon usage with the translational machinery of their tropism. For instance, we detect that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is specifically adapted to the upper respiratory tract and alveoli. Furthermore, this correspondence is specifically defined in early viral proteins. The observed tissue-specific translational efficiency could be useful for the development of antiviral therapies and vaccines.

Molecular characterization of long-term survivors of hepatocellular carcinoma

Hepatocellular carcinoma is one of the most fatal cancers, and the majority of patients die within three years. However, a small proportion of patients overcome this fatal disease and survive for more than five years. To determine the molecular characteristics of long-term survivors (survival ≥ 5 years), we analyzed the genomic and clinical data of hepatocellular carcinoma patients from The Cancer Genome Atlas and the International Cancer Genome Consortium databases, and identified molecular features that were strongly associated with the patients' prognosis. Genes involved in the cell cycle were expressed at lower levels in tumor tissues from long-term survivors than those from short-term survivors (survival ≤ 1 years). High levels of positive regulators of the G₁/S cell cycle transition (cyclin-dependent kinase 2 [CDK2], CDK4, Cyclin E2 [CCNE2], E2F1, E2F2) were potential markers of poor prognosis. Hepatocellular carcinoma patients with TP53 mutations were mainly belonged to the short-term survivor group. Abemaciclib, an FDA-approved selective inhibitor of CDK4/6, inhibited the cell proliferation and tumor growth of hepatocellular carcinoma cells in vitro and in vivo. Thus, high G₁/S transition-related gene levels and TP53 mutations are promising diagnostic biomarkers for short-term survivals, and abemaciclib may be a potential targeted drug for hepatocellular carcinoma.

An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data

Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model) can avoid it, they need strict assumptions. This undermines the very nature of things, which is very detrimental to the study of practical problems. The method proposed in this paper can apply well to the censored data, but does not need to make any additional assumptions about the original problem. It can be said that it breaks through the above two kinds of major limitations. The algorithm is a kind of inductive transfer learning method, which can fully obtain the information in the censored data, using domain-specific information implicit in each feature to enhance the generalization capability of the model. We also used two common performance metrics as criteria to judge the predictive performance differences between the models in this article and those of other mainstream models. The results show that the model proposed in this paper is 10 ∼ 15 percent higher than other mainstream models, which proves that our multi-task learning model has a great advantage in the survival analysis of cancer genes.

Comprehensive analysis of cancer breakpoints reveals signatures of genetic and epigenetic contribution to cancer genome rearrangements

Understanding mechanisms of cancer breakpoint mutagenesis is a difficult task and predictive models of cancer breakpoint formation have to this time failed to achieve even moderate predictive power. Here we take advantage of a machine learning approach that can gather important features from big data and quantify contribution of different factors. We performed comprehensive analysis of almost 630,000 cancer breakpoints and quantified the contribution of genomic and epigenomic features-non-B DNA structures, chromatin organization, transcription factor binding sites and epigenetic markers. The results showed that transcription and formation of non-B DNA structures are two major processes responsible for cancer genome fragility. Epigenetic factors, such as chromatin organization in TADs, open/closed regions, DNA methylation, histone marks are less informative but do make their contribution. As a general trend, individual features inside the groups show a relatively high contribution of G-quadruplexes and repeats and CTCF, GABPA, RXRA, SP1, MAX and NR2F2 transcription factors. Overall, the cancer breakpoint landscape can be represented by well-predicted hotspots and poorly predicted individual breakpoints scattered across genomes. We demonstrated that hotspot mutagenesis has genomic and epigenomic factors, and not all individual cancer breakpoints are just random noise but have a definite mutation signature. Besides we found a long-range action of some features on breakpoint mutagenesis. Combining omics data, cancer-specific individual feature importance and adding the distant to local features, predictive models for cancer breakpoint formation achieved 70-90% ROC AUC for different cancer types; however precision remained low at 2% and the recall did not exceed 50%. On the one hand, the power of models strongly correlates with the size of available cancer breakpoint and epigenomic data, and on the other hand finding strong determinants of cancer breakpoint formation still remains a challenge. The strength of predictive signals of each group and of each feature inside a group can be converted into cancer-specific breakpoint mutation signatures. Overall our results add to the understanding of cancer genome rearrangement processes.

Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

BACKGROUND

In osteosarcoma, the lung is the most common metastatic organ. Intensive work has been made to illuminate the pathogeny, but the specific metastatic mechanism remains unclear. Thus, we conducted the study to seek to find the key genes and critical functional pathways associated with progression and treatment in lung metastasis originating from osteosarcoma.

METHODS

Two independent datasets (GSE14359 and GSE85537) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using DAVID. Meanwhile, the protein-protein interaction (PPI) network constructed by STRING was visualized using Cytoscape. Afterwards, the key module and hub genes were extracted from the PPI network using the MCODE and cytoHubba plugin. Moreover, the raw data obtained from GSE73166 and GSE21257 were applied to verify the expression differences and conduct the survival analyses of hub genes, respectively. Finally, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape.

RESULTS

A total of 364 DEGs were identified, comprising 96 downregulated genes and 268 upregulated genes, which were mainly involved in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, MAPK signaling pathway. Subsequently, 10 hub genes were obtained and survival analysis demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma. Finally, hsa-miR-340-5p, has-miR-495-3p, and hsa-miR-96-5p were found to be most closely associated with these hub genes according to the interaction network of miRNAs and hub genes.

CONCLUSION

The key genes and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of lung metastasis originating from osteosarcoma, and provide potential diagnostic and therapeutic targets.

Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis

BACKGROUND

Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are common primary liver cancers worldwide. Liver stem cells have biopotential to differentiate into either hepatocytes and cholangiocytes, the phenotypic overlap between LIHC and CHOL has been acceptable as a continuous liver cancer spectrum. However, few studies directly investigated the underlying molecular mechanisms between LIHC and CHOL.

METHOD

To identify the candidate genes between LIHC and CHOL, three data series including GSE31370, GSE15765 and GSE40367 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape.

RESULTS

A total of 171 DEGs were identified, consisting of 49 downregulated genes and 122 upregulated genes. Compared with CHOL, the enriched functions of the DEGs mainly included steroid metabolic process, acute inflammatory response, coagulation. Meanwhile, the pathway of KEGG enrichment analyses showed that the upregulated gene(s) were mainly enriched complement and coagulation cascades, cholesterol metabolism and PPAR signaling pathway, while the downregulated gene(s) were mainly enriched in ECM-receptor interaction, focal adhesion, bile secretion. Similarly, the most significant module was identified and biological process analysis revealed that these genes were mainly enriched in regulation of blood coagulation, acute inflammatory response, complement and coagulation cascades. Finally, two (ITIH2 and APOA2) of 10 hub genes had been screened out to help differential diagnosis.

CONCLUSION

171 DEGs and two (ITIH2 and APOA2) of 10 hub genes identified in the present study help us understand the different molecular mechanisms between LIHC and CHOL, and provide candidate targets for differential diagnosis.

Screening of Methylation Gene Sites as Prognostic Signature in Lung Adenocarcinoma

PURPOSE

Most lung adenocarcinoma (LUAD) patients are diagnosed at the advanced stage and have poor prognosis. DNA methylation plays an important role in the prognosis prediction of cancers. The objective of this study was to identify new DNA methylation sites as biomarkers for LUAD prognosis.

MATERIALS AND METHODS

We downloaded DNA methylation data from The Cancer Genome Atlas data portal. Cox proportional hazard regression model and random survival forest algorithm were applied to identify the DNA-methylation sites. Methylation of sites were validated in the Gene Expression Omnibus cohorts. Function annotation were done to explore the biological function of DNA methylated sites signature.

RESULTS

Six DNA methylation sites were identified as prognosis signature. The signature yielded acceptable discrimination between the high-risk group and low-risk group. The discrimination effect of this DNA methylation signature for the OS was obvious, with a median OS of 21.89 months vs. 17.74 months for high-risk vs. low-risk groups. This prognostic prediction model was validated by the test group and GEO dataset. The predictive survival value was higher for the prognostic prediction model than that for the tumor node metastasis stage. Adjuvant hemotherapy could not affect the prediction of the signature. Functional analysis indicated that these signature genes were involved in protein binding and cytoplasm.

CONCLUSION

We identified the prognostic signature for LUAD by combining six DNA methylation sites. This could service as potential robust and specificity signature in the prognosis prediction of LUAD.

Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes

Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has been extensively studied by a large number of researchers, its prognosis is still poor. Since therapeutic advance can be obtained based on gene signatures, there is an urgent need to discover genes related to breast cancer that may help uncover the mechanisms in cancer progression. We propose a deep learning method for the discovery of breast cancer-related genes by using Capsule Network based Modeling of Multi-omics Data (CapsNetMMD). In CapsNetMMD, we make use of known breast cancer-related genes to transform the issue of gene identification into the issue of supervised classification. The features of genes are generated through comprehensive integration of multi-omics data, e.g., mRNA expression, z scores for mRNA expression, DNA methylation, and two forms of DNA copy-number alterations (CNAs). By modeling features based on the capsule network, we identify breast cancer-related genes with a significantly better performance than other existing machine learning methods. The predicted genes with prognostic values play potential important roles in breast cancer and may serve as candidates for biologists and medical scientists in the future studies of biomarkers.

CONDEL: Detecting Copy Number Variation and Genotyping Deletion Zygosity from Single Tumor Samples Using Sequence Data

Characterizing copy number variations (CNVs) from sequenced genomes is a both feasible and cost-effective way to search for driver genes in cancer diagnosis. A number of existing algorithms for CNV detection only explored part of the features underlying sequence data and copy number structures, resulting in limited performance. Here, we describe CONDEL, a method for detecting CNVs from single tumor samples using high-throughput sequence data. CONDEL utilizes a novel statistic in combination with a peel-off scheme to assess the statistical significance of genome bins, and adopts a Bayesian approach to infer copy number gains, losses, and deletion zygosity based on statistical mixture models. We compare CONDEL to six peer methods on a large number of simulation datasets, showing improved performance in terms of true positive and false positive rates, and further validate CONDEL on three real datasets derived from the 1000 Genomes Project and the EGA archive. CONDEL obtained higher consistent results in comparison with other three single sample-based methods, and exclusively identified a number of CNVs that were previously associated with cancers. We conclude that CONDEL is a powerful tool for detecting copy number variations on single tumor samples even if these are sequenced at low-coverage.

The mutREAD method detects mutational signatures from low quantities of cancer DNA

Mutational processes acting on cancer genomes can be traced by investigating mutational signatures. Because high sequencing costs limit current studies to small numbers of good-quality samples, we propose a robust, cost- and time-effective method, called mutREAD, to detect mutational signatures from small quantities of DNA, including degraded samples. We show that mutREAD recapitulates mutational signatures identified by whole genome sequencing, and will ultimately allow the study of mutational signatures in larger cohorts and, by compatibility with formalin-fixed paraffin-embedded samples, in clinical settings.

An Entropy-Based Method for Identifying Mutual Exclusive Driver Genes in Cancer

Cancer in essence is a complex genomic alteration disease which is caused by the somatic mutations during the lifetime. According to previous researches, the first step to overcome cancer is to identify driver genes which can promote carcinogenesis. However, it is still a big challenge to precisely and efficiently extract the cancer related driver genes because the nature of cancer is heterogeneous and there exists tremendously irrelevant passenger mutations which have no function impact on the cancer's development. In this work, we proposed a novel entropy-based method namely EntroRank to identify driver genes by integrating the subcellular localization information and mutual exclusive of variation frequency into the network. EntroRank can take into full consideration different properties of driver genes. Considering the modularity of driver genes, the mutated genes in the network were first clustered into different subgroups according to their located compartments. After that, the structural entropy of the gene in the subgroup was employed to measure its indispensability. Considering mutual exclusive property between driver genes in the modules, relative entropy was utilized to measure the degree of mutual exclusive between two mutated genes in terms of their variation frequency. We applied our method to three different cancers including lung, prostate, and breast cancer. The results show our method not only detect the well-known important drivers but also prioritiz the rare unknown driver genes. Besides, EntroRank can identify driver genes having mutual exclusive property. Compared with other existing methods, our method achieves a better performance for most of cancer types in terms of Precision, Recall, and Fscore.

LncRNA-PCAT1 maintains characteristics of dermal papilla cells and promotes hair follicle regeneration by regulating miR-329/Wnt10b axis

BACKGROUND

The failure of hair follicle regeneration is the major cause of alopecia, which is a highly prevalent disease worldwide. Dermal papilla (DP) cells play important role in the regulation of hair follicle regeneration. However, the molecular mechanism of how dermal papilla cells direct follicle regeneration is still to be elucidated.

METHODS

In vitro DP 3D culturing and in vivo nude mice DP sphere implanted models were used to examine the molecular regulation of DP cells and follicle regeneration. qRT-PCR and Western blotting were used to detect gene and protein expression, respectively. Immunofluorescence was used to detect the expression level of Wnt10b, Ki-67 and β-catenin. Luciferase assay was used to examine the relationship among PCAT1, miR-329 and Wnt10b. ALP activity was measured by ELISA. H&E staining was used to measure follicle growth in skin tissues.

RESULTS

Up-regulation of PCAT1 and Wnt10b, however, down-regulation of miR-329 were found in the in vitro 3D dermal papilla. Bioinformatics analysis and luciferase assays demonstrated that PCAT1 promoted Wnt10b expression by sponging miR-329. Knockdown of PCAT1 suppressed the proliferation and activity, as well as ALP and other DP markers of DP cells by targeting miR-329. Knockdown of PCAT1 regulated miR-329/Wnt10b axis to attenuate β-catenin expression and nucleus translocation to inhibit Wnt/β-catenin signaling. Furthermore, knockdown of PCAT1 suppressed DP sphere induced follicle regeneration and hair growth in nude mice.

CONCLUSION

PCAT1 maintains characteristics of DP cells by targeting miR-329 to activating Wnt/β-catenin signaling pathway, thereby promoting hair follicle regeneration.

Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

Extreme intratumour heterogeneity and driver evolution in mismatch repair deficient gastro-oesophageal cancer

Mismatch repair deficient (dMMR) gastro-oesophageal adenocarcinomas (GOAs) show better outcomes than their MMR-proficient counterparts and high immunotherapy sensitivity. The hypermutator-phenotype of dMMR tumours theoretically enables high evolvability but their evolution has not been investigated. Here we apply multi-region exome sequencing (MSeq) to four treatment-naive dMMR GOAs. This reveals extreme intratumour heterogeneity (ITH), exceeding ITH in other cancer types >20-fold, but also long phylogenetic trunks which may explain the exquisite immunotherapy sensitivity of dMMR tumours. Subclonal driver mutations are common and parallel evolution occurs in RAS, PIK3CA, SWI/SNF-complex genes and in immune evasion regulators. MSeq data and evolution analysis of single region-data from 64 MSI GOAs show that chromosome 8 gains are early genetic events and that the hypermutator-phenotype remains active during progression. MSeq may be necessary for biomarker development in these heterogeneous cancers. Comparison with other MSeq-analysed tumour types reveals mutation rates and their timing to determine phylogenetic tree morphologies.

Identifying "Many-to-Many" Relationships between Gene-Expression Data and Drug-Response Data via Sparse Binary Matching

Identifying gene-drug patterns is a critical step in pharmacology for unveiling disease mechanisms and drug discovery. The availability of high-throughput technologies accumulates massive large-scale pharmacological and genomic data, and thus provides a new substantial opportunity to deeply understand how the oncogenic genes and the therapeutic drugs relate to each other. However, most previous studies merely used the pharmacological and genomic datasets without any prior knowledge to infer the gene-drug patterns. Here, we proposed a novel network-guided sparse binary matching model (NSBM) to decode these relationships hidden in the datasets. Not only the large-scale gene-expression data and drug-response data are jointly analyzed in our method, but also the additional prior information of genes and drugs are integrated into the form of network-based regularization. The essential structure of the NSBM model is a convex quadratic minimization problem with network-based penalties. It was demonstrated to be superior when compared with two benchmark methods through extensive experiments on both synthetic and empirical data. Posterior validation, including gene-ontology and enrichment analysis, confirmed the effectiveness of NSBM in revealing gene-drug patterns on a large-scale heterogeneous data source.

Prognostication of uveal melanoma is simple and highly predictive using The Cancer Genome Atlas (TCGA) classification: A review

Purpose

The cancer genome atlas (TCGA) is a comprehensive project supported by the National Cancer Institute (NCI) in the United States to explore molecular alterations in cancer, including uveal melanoma (UM). This led to TCGA classification for UM. In this report, we review the American Joint Committee on Cancer (AJCC) classification and TCGA classification for UM from the NCI's Center for Cancer Genomics (NCI CCG) (based on enucleation specimens [n = 80 eyes]) and from Wills Eye Hospital (WEH) (based on fine needle aspiration biopsy [FNAB] specimens [n = 658 eyes]). We then compare accuracy and predictability of AJCC versus (vs.) TCGA.

Methods

Review of published reports on AJCC and TCGA classification for UM was performed. Outcomes based on AJCC 7th and 8th editions were assessed. For TCGA, UM was classified based on chromosomes 3 and 8 findings including disomy 3 (D3), monosomy 3 (M3), disomy 8 (D8), 8q gain (8qG), or 8q gain multiple (8qGm) and combined into four classes including Class A (D3/D8), Class B (D3/8qG), Class C (M3/8qG), and Class D (M3/8qGm). Outcomes of metastasis and death were explored and a comparison (AJCC vs. TCGA) was performed.

Results

In the NCI CCG study, there were 80 eyes with UM sampled by enucleation (n = 77), resection (n = 2), or orbitotomy (n = 1) and analysis revealed four distinct genetic classes. Metastasis and death outcomes were subsequently evaluated per class in the WEH study. The WEH study reviewed 658 eyes with UM, sampled by FNAB, and found Class A (n = 342, 52%), B (n = 91, 14%), C (n = 118, 18%), and D (n = 107, 16%). Comparison by increasing class (A vs. B vs. C vs. D) revealed older mean patient age (P < 0.001), worse entering visual acuity (P < 0.001), greater distance from the optic disc (P < 0.001), larger tumor diameter (P < 0.001), and greater tumor thickness (P < 0.001). Regarding outcomes, more advanced TCGA class demonstrated increased 5-year risk for metastasis (4% vs. 20% vs. 33% vs. 63%,P < 0.001) with corresponding increasing hazard ratio (HR) (1.0 vs. 4.1, 10.1, 30.0,P= 0.01 for B vs. A andP < 0.001 for C vs. A and D vs. A) as well as increased 5-year estimated risk for death (1% vs. 0% vs. 9% vs. 23%,P < 0.001) with corresponding increasing HR (1 vs. NA vs. 3.1 vs. 13.7,P= 0.11 for C vs. A andP < 0.001 for D vs. A). Comparison of AJCC to TCGA classification revealed TCGA was superior in prediction of metastasis and death from UM.

Conclusion

TCGA classification for UM is simple, accurate, and highly predictive of melanoma-related metastasis and death, more so than the AJCC classification.

An immunoevasive strategy through clinically-relevant pan-cancer genomic and transcriptomic alterations of JAK-STAT signaling components

BACKGROUND

Since its discovery almost three decades ago, the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway has paved the road for understanding inflammatory and immunity processes related to a wide range of human pathologies including cancer. Several studies have demonstrated the importance of JAK-STAT pathway components in regulating tumor initiation and metastatic progression, yet, the extent of how genetic alterations influence patient outcome is far from being understood.

METHODS

Focusing on 133 genes involved in JAK-STAT signaling, we investigated genomic, transcriptomic and clinical profiles of over 18,000 patients representing 21 diverse cancer types. We identified a core set of 28 putative gain- or loss-of-function JAK-STAT genes that correlated with survival outcomes using Cox proportional hazards regression and Kaplan-Meier analyses. Differential expression analyses between high- and low-expressing patient groups were performed to evaluate the consequences of JAK-STAT misexpression.

RESULTS

We found that copy number alterations underpinning transcriptional dysregulation of JAK-STAT pathway genes differ within and between cancer types. Integrated analyses uniting genomic and transcriptomic datasets revealed a core set of JAK-STAT pathway genes that correlated with survival outcomes in brain, renal, lung and endometrial cancers. High JAK-STAT scores were associated with increased mortality rates in brain and renal cancers, but not in lung and endometrial cancers where hyperactive JAK-STAT signaling is a positive prognostic factor. Patients with aberrant JAK-STAT signaling demonstrated pan-cancer molecular features associated with misexpression of genes in other oncogenic pathways (Wnt, MAPK, TGF-β, PPAR and VEGF). Brain and renal tumors with hyperactive JAK-STAT signaling had increased regulatory T cell gene (Treg) expression. A combined model uniting JAK-STAT and Tregs allowed further delineation of risk groups where patients with high JAK-STAT and Treg scores consistently performed the worst.

CONCLUSION

Providing a pan-cancer perspective of clinically-relevant JAK-STAT alterations, this study could serve as a framework for future research investigating anti-tumor immunity using combination therapy involving JAK-STAT and immune checkpoint inhibitors.

Defining the molecular evolution of extrauterine high grade serous carcinoma

OBJECTIVE

High grade serous carcinoma (HGSC) is the most common and most aggressive, subtype of epithelial ovarian cancer. It presents as advanced stage disease with poor prognosis. Recent pathological evidence strongly suggests HGSC arises from the fallopian tube via the precursor lesion; serous tubal intraepithelial carcinoma (STIC). However, further definition of the molecular evolution of HGSC has major implications for both clinical management and research. This study aims to more clearly define the molecular pathogenesis of HGSC.

METHODS

Six cases of HGSC were identified at the Northern Ireland Gynaecological Cancer Centre (NIGCC) that each contained ovarian HGSC (HGSC), omental HGSC (OMT), STIC, normal fallopian tube epithelium (FTE) and normal ovarian surface epithelium (OSE). The relevant formalin-fixed paraffin embedded (FFPE) tissue samples were retrieved from the pathology archive via the Northern Ireland Biobank following attaining ethical approval (NIB11:005). Full microarray-based gene expression profiling was performed on the cohort. The resulting data was analysed bioinformatically and the results were validated in a HGSC-specific in-vitro model.

RESULTS

The carcinogenesis of HGSC was investigated and showed the molecular profile of HGSC to be more closely related to normal FTE than OSE. STIC lesions also clustered closely with HGSC, indicating a common molecular origin.

CONCLUSION

This study provides strong evidence suggesting that extrauterine HGSC arises from the fimbria of the distal fallopian tube. Furthermore, several potential pathways were identified which could be targeted by novel therapies for HGSC. These findings have significant translational relevance for both primary prevention and clinical management of the disease.

Distinct molecular etiologies of male and female hepatocellular carcinoma

BACKGROUND

Sex-differences in cancer occurrence and mortality are evident across tumor types; men exhibit higher rates of incidence and often poorer responses to treatment. Targeted approaches to the treatment of tumors that account for these sex-differences require the characterization and understanding of the fundamental biological mechanisms that differentiate them. Hepatocellular Carcinoma (HCC) is the second leading cause of cancer death worldwide, with the incidence rapidly rising. HCC exhibits a male-bias in occurrence and mortality, but previous studies have failed to explore the sex-specific dysregulation of gene expression in HCC.

METHODS

Here, we characterize the sex-shared and sex-specific regulatory changes in HCC tumors in the TCGA LIHC cohort using combined and sex-stratified differential expression and eQTL analyses.

RESULTS

By using a sex-specific differential expression analysis of tumor and tumor-adjacent samples, we uncovered etiologically relevant genes and pathways differentiating male and female HCC. While both sexes exhibited activation of pathways related to apoptosis and cell cycle, males and females differed in the activation of several signaling pathways, with females showing PPAR pathway enrichment while males showed PI3K, PI3K/AKT, FGFR, EGFR, NGF, GF1R, Rap1, DAP12, and IL-2 signaling pathway enrichment. Using eQTL analyses, we discovered germline variants with differential effects on tumor gene expression between the sexes. 24.3% of the discovered eQTLs exhibit differential effects between the sexes, illustrating the substantial role of sex in modifying the effects of eQTLs in HCC. The genes that showed sex-specific dysregulation in tumors and those that harbored a sex-specific eQTL converge in clinically relevant pathways, suggesting that the molecular etiologies of male and female HCC are partially driven by differential genetic effects on gene expression.

CONCLUSIONS

Sex-stratified analyses detect sex-specific molecular etiologies of HCC. Overall, our results provide new insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and provide a framework for future studies on sex-biased cancers.

Novel candidates in early-onset familial colorectal cancer

In 20-30% of patients suspected of a familial colorectal cancer (CRC) syndrome, no underlying genetic cause is detected. Recent advances in whole exome sequencing have generated evidence for new CRC-susceptibility genes including POLE, POLD1 and NTHL1¸ but many patients remain unexplained. Whole exome sequencing was performed on DNA from nine patients from five different families with familial clusters of CRC in which traditional genetic testing failed to yield a diagnosis. Variants were filtered by minor allele frequencies, followed by prioritization based on in silico prediction tools, and the presence in cancer susceptibility genes or genes in cancer-associated pathways. Effects of frameshift variants on protein structure were modeled using I-Tasser. One known pathogenic variant in POLD1 was detected (p.S478N), together with variants in 17 candidate genes not previously associated with CRC. Additional in silico analysis using SIFT, PROVEAN and PolyPhen on the 14 missense variants indicated a possible damaging effect in nine of 14 variants. Modeling of the insertions/deletions showed a damaging effect of two variants in NOTCH2 and CYP1B1. One family was explained by a mutation in a known familial CRC gene. In the remaining four families, the most promising candidates found are a frameshift NOTCH2 and a missense RAB25 variant. This study provides potential novel candidate variants in unexplained familial CRC patients, however, functional validation is imperative to confirm the role of these variants in CRC tumorigenesis. Additionally, while whole exome sequencing enables detection of variants throughout the exome, other causes explaining the familial phenotype such as multiple single nucleotide polymorphisms accumulating to a polygenic risk or epigenetic events, might be missed with this approach.

Defining the Boundary Between Myelodysplastic Syndromes and Myeloproliferative Neoplasms

In this article we provide a practical and comprehensive review of myeloid neoplasms with overlapping myelodysplastic (MDS) and myeloproliferative (MPN) features, with emphasis on recent updates in classification, particularly the utility of morphologic, cytogenetic, and molecular findings in better defining and classifying these disease entities. We provide the reader with a summary of the most recent developments and updates that have helped further our understanding of the genomic landscape, clinicopathologic features, and prognostic elements of myeloid neoplasms with MDS/MPN features.

Prognostic subclass of intrahepatic cholangiocarcinoma by integrative molecular-clinical analysis and potential targeted approach

BACKGROUND

Although molecular characterization of iCCA has been studied recently, integrative analysis of molecular and clinical characterization has not been fully established. If molecular features of iCCA can be predicted based on clinical findings, we can approach to distinguish targeted treatment. We analyzed RNA sequencing data annotated with clinicopathologic data to clarify molecular-specific clinical features and to evaluate potential therapies for molecular subtypes.

METHODS

We performed next-generation RNA sequencing of 30 surgically resected iCCA from Korean patients and the clinicopathologic features were analyzed. The RNA sequences from 32 iCCA resected from US patients were used for validation.

RESULTS

Patients were grouped into two subclasses on the basis of unsupervised clustering, which showed a difference in 5-year survival rates (48.5% vs 14.2%, p = 0.007) and similar survival outcome in the US samples. In subclass B (poor prognosis), both data sets were similar in higher carcinoembryonic antigen and cancer antigen 19-9 levels, underlying cholangitis, and bile duct-type pathology; in subclass A (better prognosis), there was more frequent viral hepatitis and cholangiolar-type pathology. On pathway analysis, subclass A had enriched liver-related signatures. Subclass B had enriched inflammation-related and TP53 pathways, with more frequent KRAS mutations. CCA cell lines with similar gene expression patterns of subclass A were sensitive to gemcitabine.

CONCLUSIONS

Two molecular subtypes of iCCA with distinct clinicopathological differences were identified. Knowledge of clinical and pathologic characteristics can predict molecular subtypes, and knowledge of different subtype signaling pathways may lead to more rational, targeted approaches to treatment.

Comprehensive validation of liquid-based cytology specimens for next-generation sequencing in cancer genome analysis

In addition to conventional cytology, liquid-based cytology (LBC) is also used for immunocytochemistry and gene analysis. However, an appropriate method to obtain high quality DNA for next-generation sequencing (NGS) using LBC specimens remains controversial. We determined the optimal conditions for fixation with an alcohol-based fixative for LBC and DNA extraction using cultured cancer cell lines and clinical specimens. The extracted DNA was processed for NGS after the DNA quality was confirmed based on the DNA concentration and degree of degradation. The optimal conditions for cultured cells to obtain high quality DNA were to fix the cells at a density of 6 × 10³ or 2 × 10⁴ cells/mL and to use the magnetic bead-based DNA extraction method. Even after storing the fixed cells for 90 days, DNA extracted using the above and other extraction kits, including membrane-based methods, did not undergo degradation. Furthermore, 5-year-old residual LBC samples demonstrated high DNA quality that was suitable for NGS. Furthermore, a cancer genome panel analysis was successfully performed with DNA extracted from cultured cells fixed at 6 × 10³ cells/mL for 90 days, and with DNA from residual LBC samples even after 1 year of storage. Residual LBC samples may be a useful source of DNA for clinical NGS to promote genome-based cancer medicine.

Genetic predisposition to colorectal cancer: syndromes, genes, classification of genetic variants and implications for precision medicine

This article reviews genes and syndromes associated with predisposition to colorectal cancer (CRC), with an overview of gene variant classification. We include updates on the application of preventive and therapeutic measures, focusing on the use of non-steroidal anti-inflammatory drugs (NSAIDs) and immunotherapy. Germline pathogenic variants in genes conferring high or moderate risk to cancer are detected in 6-10% of all CRCs and 20% of those diagnosed before age 50. CRC syndromes can be subdivided into nonpolyposis and polyposis entities, the most common of which are Lynch syndrome and familial adenomatous polyposis, respectively. In addition to known and novel genes associated with highly penetrant CRC risk, identification of pathogenic germline variants in genes associated with moderate-penetrance cancer risk and/or hereditary cancer syndromes not traditionally linked to CRC may have an impact on genetic testing, counseling, and surveillance. The use of multigene panels in genetic testing has exposed challenges in the classification of variants of uncertain significance. We provide an overview of the main classification systems and strategies for improving these. Finally, we highlight approaches for integrating chemoprevention in the care of individuals with genetic predisposition to CRC and use of targeted agents and immunotherapy for treatment of mismatch repair-deficient and hypermutant tumors. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Genomic Signature of Driver Genes Identified by Target Next-Generation Sequencing in Chinese Non-Small Cell Lung Cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) is one of the most common human malignancies and the leading cause of cancer-related death. Over the past few decades, genomic alterations of cancer driver genes have been identified in NSCLC, and molecular testing and targeted therapies have become standard care for lung cancer patients. Here we studied the unique genomic profile of driver genes in Chinese patients with NSCLC by next-generation sequencing (NGS) assay.

MATERIALS AND METHODS

A total of 1,200 Chinese patients with NSCLC were enrolled in this study. The median age was 60 years (range: 26-89), and 83% cases were adenocarcinoma. NGS-based genomic profiling of major lung cancer-related genes was performed on formalin-fixed paraffin-embedded tumor samples and matched blood.

RESULTS

Approximately 73.9% of patients with NSCLC harbored at least one actionable alteration recommended by the National Comprehensive Cancer Network guideline, including epidermal growth factor receptor (EGFR), ALK, ERBB2, MET, BRAF, RET, and ROS1. Twenty-seven patients (2.2%) harbored inherited germline mutations of cancer susceptibility genes. The frequencies of EGFR genomic alterations (both mutations and amplification) and ALK rearrangement were identified as 50.1% and 7.8% in Chinese NSCLC populations, respectively, and significantly higher than the Western population. Fifty-six distinct uncommon EGFR mutations other than L858R, exon19del, exon20ins, or T790M were identified in 18.9% of patients with EGFR-mutant NSCLC. About 7.4% of patients harbored both sensitizing and uncommon mutations, and 11.6% of patients harbored only uncommon EGFR mutations. The uncommon EGFR mutations more frequently combined with the genomic alterations of ALK, CDKN2A, NTRK3, TSC2, and KRAS. In patients <40 years of age, the ALK-positive percentage was up to 28.2%. Moreover, 3.2% of ALK-positive patients harbored multi ALK rearrangements, and seven new partner genes were identified.

CONCLUSION

More unique features of cancer driver genes in Chinese NSCLC were identified by next-generation sequencing. These findings highlighted that NGS technology is more feasible and necessary than other molecular testing methods, and suggested that the special strategies are needed for drug development and targeted therapy for Chinese patients with NSCLC.

IMPLICATIONS FOR PRACTICE

Molecular targeted therapy is now the standard first-line treatment for patients with advanced non-small cell lung cancer (NSCLC). Samples of 1,200 Chinese patients with NSCLC were analyzed through next-generation sequencing to characterize the unique feature of uncommon EGFR mutations and ALK fusion. The results showed that 7.4% of EGFR-mutant patients harbored both sensitizing and uncommon mutations and 11.6% harbored only uncommon mutations. Uncommon EGFR mutations more frequently combined with the genomic alterations of ALK, CDKN2A, NTRK3, TSC2, and KRAS. ALK fusion was more common in younger patients, and the frequency decreased monotonically with age. 3.2% of ALK-positive patients harbored multi ALK rearrangement, and seven new partner genes were identified.

MicroRNAs and their role in environmental chemical carcinogenesis

MicroRNAs (miRNAs) are a class of small, noncoding RNA species that play crucial roles across many biological processes and in the pathogenesis of major diseases, including cancer. Recent studies suggest that the expression of miRNA is altered by certain environmental chemicals, including metals, organic pollutants, cigarette smoke, pesticides and carcinogenic drugs. In addition, extensive studies have indicated the existence and importance of miRNA in different cancers, suggesting that cancer-related miRNAs could serve as potential markers for chemically induced cancers. The altered expression of miRNA was considered to be a vital pathogenic role in xenobiotic-induced cancer development. However, the significance of miRNA in the etiology of cancer and the exact mechanisms by which environmental factors alter miRNA expression remain relatively unexplored. Hence, understanding the interaction of miRNAs with environmental chemicals will provide important information on mechanisms underlying the pathogenesis of chemically induced cancers, and effectively diagnose and treat human cancers resulting from chronic or acute carcinogen exposure. This study presents the current evidence that the miRNA deregulation induced by various chemical carcinogens, different cancers caused by environmental carcinogens and the potentially related genes in the onset or progression of cancer. For each carcinogen, the specifically expressed miRNA may be considered as the early biomarkers of the cancer process. In this review, we also summarize various target genes of the altered miRNA, oncogenes or anti-oncogenes, and the existing evidence regarding the gene regulation mechanisms of cancer caused by environmentally induced miRNA alteration. The future perspective of miRNA may become attractive targets for the diagnosis and treatment of carcinogen-induced cancer.

Genetics for the Women's Health Trainee: A Five-Module Curriculum

Introduction

Genetics is ubiquitous in OB-GYN. However, data suggest that trainees feel underprepared to counsel patients about genetic testing, the nuances of which are becoming increasingly complicated. We sought to develop and implement a genetics curriculum for OB-GYN residents.

Methods

This five-module (screening for fetal aneuploidy, prenatal diagnostic testing, prenatal carrier screening, pedigrees, and cancer genetics), interactive, case-based curriculum is linked to Council on Resident Education in Obstetrics and Gynecology objectives and can stand alone or work as part of an ultrasound or obstetrics rotation. Each module, containing objectives, assigned readings, and cases with answers, is used in a small-group format and can be completed in 20-30 minutes prior to the start of a clinical day. Modules were implemented at two academic centers with first-year OB-GYN residents. Qualitative real-time feedback and summative quantitative feedback from OB-GYN residents were obtained.

Results

Twenty-one OB-GYN residents completed the curriculum, which was well received by trainees and program directors. All residents (100%) felt the curriculum increased knowledge of prenatal genetics and felt more comfortable counseling patients after completion. Seventy-three percent enjoyed the discussion/case-based format; associated articles were found helpful by 100% of trainees. Facilitators enjoyed teaching the curriculum and felt learner knowledge improved dramatically.

Discussion

These low-cost modules were easy to implement and resulted in increased knowledge and confidence in prenatal and cancer genetics. Designed to stand alone and take as little as 20 minutes, the modules provide a helpful adjunct to a women's health rotation or didactic curriculum.

Loss at 16q22.1 identified as a risk factor for intrahepatic recurrence in hepatocellular carcinoma and screening of differentially expressed genes

Copy number alteration (CNA) of chromosome 16, a frequent genetic event in tumors including hepatocellular carcinoma (HCC), has been associated with HCC etiology of hepatitis B virus (HBV) and with clinical outcomes in multiple types of cancer. This study identified CNAs in chromosome 16 in relation to intrahepatic recurrence of HCC in a population with high HBV prevalence, and further screened for differentially expressed genes in recurrence-related CNAs. Array comparative genomic hybridization and expression arrays were used to detect CNAs and gene expression differences, respectively. The associations between CNAs and intrahepatic recurrence were analyzed on 66 patients, follow-up period of 3-73 months. One hundred and nine cases were further evaluated regarding the differentially expressed genes. Losses at 16q and 16p were detected in 62.1% and 51.5% of the 66 cases, respectively. The most recurrent CNAs (with frequency >20%) were losses at 16p13.3-13.2, 16p13.11, 16q11.2-22.1, 16q22.1, 16q22.2-24.2 and 16q24.2. Of the CNAs, 16q22.1 loss was significantly associated with unfavorable intrahepatic recurrence-free survival (P = 0.025). Multivariate Cox analysis identified 16q22.1 loss as an independent risk factor for intrahepatic recurrence (HR = 2.32, 95% CI = 1.26-4.27). A panel of 21 genes, including TRADD, PSMB10, THAP11, CTCF and ESRP2, were significantly downregulated in HCCs with 16q22.1 loss compared to those without the loss. These results suggest that loss at 16q22.1 was associated with increased risk for intrahepatic recurrence of HCC, at least in the HBV-prevalence population. Multiple downregulated genes correlated with the loss were screened.

Transcriptional Regulation in Prostate Cancer

Prostate cancer development involves corruption of the normal prostate transcriptional network, following deregulated expression or mutation of key transcription factors. Here, we provide an overview of the transcription factors that are important in normal prostate homeostasis (NKX3-1, p63, androgen receptor [AR]), primary prostate cancer (ETS family members, c-MYC), castration-resistant prostate cancer (AR, FOXA1), and AR-independent castration-resistant neuroendocrine prostate cancer (RB1, p53, N-MYC). We use functional (in vitro and in vivo) as well as clinical data to discuss evidence that unveils their roles in the initiation and progression of prostate cancer, with an emphasis on results of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq).

Creutzfeldt astrocytes may be seen in IDH-wildtype glioblastoma and retain expression of DNA repair and chromatin binding proteins

Astrocytes with multiple micronuclei ("Creutzfeldt cells") in a brain biopsy are classically associated with demyelinating disease. However, glioblastoma may also have prominent Creutzfeldt astrocytes, along with granular mitoses. Therefore, Creutzfeldt cells may raise the diagnostic dilemma of high-grade glioma vs tumefactive demyelination. While cases of glioblastoma (GBM) with Creutzfeldt astrocytes have been reported, their clinicopathologic spectrum and genetic features are not understood. Studies have proposed that micronuclei in Creutzfeldt cells are a consequence of DNA damage, or may be susceptible to DNA damage and chromothripsis, but their biology in the context of glioblastoma remains unclear. Based on a challenging index case of GBM with mild hypercellularity, Creutzfeldt astrocytes, and granular mitoses on biopsy, we searched our archives for additional cases with similar histopathologic features. We identified 13 cases, reviewed their clinico-radiologic and pathologic features, and examined them for recurrent genetic alterations via NGS (9 cases) and for evidence of DNA damage by immunohistochemistry for DNA repair and chromatin remodeling proteins. We found that Creutzfeldt cell-rich GBMs were IDH-wildtype with no recurring genetic alterations. To test our hypothesis that micronuclei demonstrate loss of DNA repair or chromatin remodeling proteins, we examined the expression of various proteins (MDM2, p53, MLH1, MSH2, PMS2, MSH6, ATRX, INI1, SATB2, Ki67, pHH3) in Creutzfeldt cell rich-GBM. There was intact expression of DNA repair and chromatin remodeling proteins, with accumulation of p53 and reduced MDM2 expression within micronuclei. In contrast, granular mitoses showed pHH3 expression, confirming these cells are undergoing mitotic division, with no accumulation of p53 and reduced expression of DNA repair proteins. Our results emphasize that Creutzfeldt cells are part of the morphologic spectrum of IDH-wildtype glioblastoma. We did not find a role for DNA damage in the generation of Creutzfeldt cells, as both DNA repair and chromatin remodeling protein expression was retained in these cells.

The NETest: The Clinical Utility of Multigene Blood Analysis in the Diagnosis and Management of Neuroendocrine Tumors

The neuroendocrine neoplasms test (NETest) is a multianalyte liquid biopsy that measures neuroendocrine tumor gene expression in blood. This unique signature precisely defines the biological activity of an individual tumor in real time. The assay meets the 3 critical requirements of an optimal biomarker: diagnostic accuracy, prognostic value, and predictive therapeutic assessment. NETest performance metrics are sensitivity and specificity and in head-to-head comparison are 4-fold to 10-fold more accurate than chromogranin A. NETest accurately identifies completeness of surgery and response to somatostatin analogs. Clinical registry data demonstrate significant clinical utility in watch/wait programs.

An automated microfluidic gene-editing platform for deciphering cancer genes

Gene-editing techniques such as RNA-guided endonuclease systems are becoming increasingly popular for phenotypic screening. Such screens are normally conducted in arrayed or pooled formats. There has been considerable interest in recent years to find new technological methods for conducting these gene-editing assays. We report here the first digital microfluidic method that can automate arrayed gene-editing in mammalian cells. Specifically, this method was useful in culturing lung cancer cells for up to six days, as well as implementing automated gene transfection and knockout procedures. In addition, a standardized imaging pipeline to analyse fluorescently labelled cells was also designed and implemented during these procedures. A gene editing assay for interrogating the MAPK/ERK pathway was performed to show the utility of our platform and to determine the effects of knocking out the RAF1 gene in lung cancer cells. In addition to gene knockout, we also treated the cells with an inhibitor, Sorafenib Tosylate, to determine the effects of enzymatic inhibition. The combination of enzymatic inhibition and guide targeting on device resulted in lower drug concentrations for achieving half-inhibitory effects (IC50) compared to cells treated only with the inhibitor, confirming that lung cancer cells are being successfully edited on the device. We propose that this system will be useful for other types of gene-editing assays and applications related to personalized medicine.

The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms

The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.

A proteomic landscape of diffuse-type gastric cancer

The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1-3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.

Ethnic specific differences in endocrine neoplasms: The role of susceptibility genes

Ethnic disparity in disease incidence, prevalence, and outcome has been documented for a variety of diseases and cancers. Dr. LaSalle D. Leffall was one of the first to note that genetic susceptibility is one important aspect that needed to be studied to better understand cancer disparity. In this article, we cover disparity in endocrine neoplasm presentation and new, emerging genetic data that may explain this disparity.

Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control

N6-methyladenosine (m6A) is an abundant internal RNA modification in both coding and non-coding RNAs that is catalysed by the METTL3-METTL14 methyltransferase complex. However, the specific role of these enzymes in cancer is still largely unknown. Here we define a pathway that is specific for METTL3 and is implicated in the maintenance of a leukaemic state. We identify METTL3 as an essential gene for growth of acute myeloid leukaemia cells in two distinct genetic screens. Downregulation of METTL3 results in cell cycle arrest, differentiation of leukaemic cells and failure to establish leukaemia in immunodeficient mice. We show that METTL3, independently of METTL14, associates with chromatin and localizes to the transcriptional start sites of active genes. The vast majority of these genes have the CAATT-box binding protein CEBPZ present at the transcriptional start site, and this is required for recruitment of METTL3 to chromatin. Promoter-bound METTL3 induces m6A modification within the coding region of the associated mRNA transcript, and enhances its translation by relieving ribosome stalling. We show that genes regulated by METTL3 in this way are necessary for acute myeloid leukaemia. Together, these data define METTL3 as a regulator of a chromatin-based pathway that is necessary for maintenance of the leukaemic state and identify this enzyme as a potential therapeutic target for acute myeloid leukaemia.

Automated deconvolution of structured mixtures from heterogeneous tumor genomic data

With increasing appreciation for the extent and importance of intratumor heterogeneity, much attention in cancer research has focused on profiling heterogeneity on a single patient level. Although true single-cell genomic technologies are rapidly improving, they remain too noisy and costly at present for population-level studies. Bulk sequencing remains the standard for population-scale tumor genomics, creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data. All such methods are limited to coarse approximations of only a few cell subpopulations, however. In prior work, we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing. We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data, as well as the ability to process quantitative RNA expression data, and heterogeneous combinations of RNA and CNV data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas (TCGA). Source code is available at https://github.com/tedroman/WSCUnmix.

Cancer Subtype Discovery Based on Integrative Model of Multigenomic Data

One major goal of large-scale cancer omics study is to understand molecular mechanisms of cancer and find new biomedical targets. To deal with the high-dimensional multidimensional cancer omics data (DNA methylation, mRNA expression, etc.), which can be used to discover new insight on identifying cancer subtypes, clustering methods are usually used to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrate these data and map them into an effective low-dimensional subspace. In this paper, we develop a dimension-reduction and data-integration method for indentifying cancer subtypes, named Scluster. First, Scluster, respectively, projects the different original data into the principal subspaces by an adaptive sparse reduced-rank regression method. Then, a fused patient-by-patient network is obtained for these subgroups through a scaled exponential similarity kernel method. Finally, candidate cancer subtypes are identified using spectral clustering method. We demonstrate the efficiency of our Scluster method using three cancers by jointly analyzing mRNA expression, miRNA expression, and DNA methylation data. The evaluation results and analyses show that Scluster is effective for predicting survival and identifies novel cancer subtypes of large-scale multi-omics data.

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.

Two different, mutually exclusively distributed, TP53 mutations in ovarian and peritoneal tumor tissues of a serous ovarian cancer patient: indicative for tumor origin?

High-grade serous ovarian cancer (HGSOC) is characterized by a TP53 mutation rate of up to 96.7% and associated with a more aggressive tumor biology. The origin of HGSOC is thought to arise either from fallopian tube secretory cells or the ovarian surface epithelium/inclusion cysts, the former with more evidence. Peritoneal tumor spread is heterogeneous, either excessive in the peritoneum (with miliary appearance) or more confined to the ovaries with only few (bigger and exophytically growing) peritoneal implants. Using RNA sequencing and DNA digital droplet polymerase chain reaction (PCR), we identified two different functional TP53 mutations in one HGSOC patient: one exclusively in the ovarian tumor mass and the other exclusively in ascites tumor cells, peritoneal tumor masses, and a lymph node metastasis. In blood, both mutations could be detected, the one from the peritoneal tumors with much higher frequency, presumably because of the higher tumor load. We conclude that this mutually exclusive distribution of two different TP53 mutations in different tumor tissues indicates the development of two independent carcinomas in the peritoneal cavity, probably one originating from a precancerous lesion in the fallopian tube and the other from the ovaries. In addition, in the patient's ascites CD45 and EpCAM, double-positive cells were found-proliferating but testing negative for the above-mentioned TP53 mutations. This mutually exclusive distribution of two TP53 mutations is probably further evidence that HGSOC can originate either from the fallopian tube or (more seldom) the ovaries, the former more prone for excessive peritoneal tumor spread.

Transplantation of engineered organoids enables rapid generation of metastatic mouse models of colorectal cancer

Colorectal cancer (CRC) is a leading cause of death in the developed world, yet facile preclinical models that mimic the natural stages of CRC progression are lacking. Through the orthotopic engraftment of colon organoids we describe a broadly usable immunocompetent CRC model that recapitulates the entire adenoma-adenocarcinoma-metastasis axis in vivo. The engraftment procedure takes less than 5 minutes, shows efficient tumor engraftment in two-thirds of mice, and can be achieved using organoids derived from genetically engineered mouse models (GEMMs), wild-type organoids engineered ex vivo, or from patient-derived human CRC organoids. In this model, we describe the genotype and time-dependent progression of CRCs from adenocarcinoma (6 weeks), to local disseminated disease (11-12 weeks), and spontaneous metastasis (>20 weeks). Further, we use the system to show that loss of dysregulated Wnt signaling is critical for the progression of disseminated CRCs. Thus, our approach provides a fast and flexible means to produce tailored CRC mouse models for genetic studies and pre-clinical investigation.

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .