scholarly journals A molecular portrait of microsatellite instability across multiple cancers

2016 ◽  
Author(s):  
Isidro Cortes-Ciriano ◽  
Sejoon Lee ◽  
Woong-Yang Park ◽  
Tae-Min Kim ◽  
Peter J. Park
Keyword(s):  
Microsatellite Instability ◽  
High Sensitivity ◽  
Tumor Type ◽  
Sequencing Data ◽  
Molecular Fingerprints ◽  
Dna Mismatch ◽  
Oncogenic Pathways ◽  
Cancer Types ◽  
Tumor Types ◽  
Pan Cancer

ABSTRACTMicrosatellite instability (MSI) refers to the hypermutability of the cancer genome due to impaired DNA mismatch repair. Although MSI has been studied for decades, the large amount of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyze ~8000 exome and ~1000 whole-genome pairs across 23 cancer types. Our pan-cancer analysis reveals that the prevalence of MSI events is highly variable within and across tumor types including some in which MSI is not typically examined. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI events. Finally, we propose an exomebased predictive model for the MSI phenotype that achieves high sensitivity and specificity. These results advance our understanding of the genomic drivers and consequences of MSI, and a comprehensive catalog of tumor-type specific MSI loci we have generated enables efficient panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.

scholarly journals Predicting master transcription factors from pan-cancer expression data

2019 ◽  
Author(s):  
Jessica Reddy ◽  
Marcos A. S. Fonseca ◽  
Rosario I Corona ◽  
Robbin Nameki ◽  
Felipe Segato Dezem ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Regulatory Elements ◽  
Ca 125 ◽  
Tumor Type ◽  
Expression Data ◽  
Primary Tumors ◽  
Cancer Types ◽  
Tumor Types ◽  
Pan Cancer

The function of critical developmental regulators can be subverted by cancer cells to control expression of oncogenic transcriptional programs. These "master transcription factors" (MTFs) are often essential for cancer cell survival and represent vulnerabilities that can be exploited therapeutically. The current approaches to identify candidate MTFs examine super-enhancer associated transcription factor-encoding genes with high connectivity in network models. This relies on chromatin immunoprecipitation-sequencing (ChIP-seq) data, which is technically challenging to obtain from primary tumors, and is currently unavailable for many cancer types and clinically relevant subtypes. In contrast, gene expression data are more widely available, especially for rare tumors and subtypes where MTFs have yet to be discovered. We have developed a predictive algorithm called CaCTS (Cancer Core Transcription factor Specificity) to identify candidate MTFs using pan-cancer RNA-sequencing data from The Cancer Genome Atlas. The algorithm identified 273 candidate MTFs across 34 tumor types and recovered known tumor MTFs. We also made novel predictions, including for cancer types and subtypes for which MTFs have not yet been characterized. Clustering based on MTF predictions reproduced anatomic groupings of tumors that share 1-2 lineage-specific candidates, but also dictated functional groupings, such as a squamous group that comprised five tumor subtypes sharing 3 common MTFs. PAX8, SOX17, and MECOM were candidate factors in high-grade serous ovarian cancer (HGSOC), an aggressive tumor type where the core regulatory circuit is currently uncharacterized. PAX8, SOX17, and MECOM are required for cell viability and lie proximal to super-enhancers in HGSOC cells. ChIP-seq revealed that these factors co-occupy HGSOC regulatory elements globally and co-bind at critical gene loci including MUC16 (CA-125). Addiction to these factors was confirmed in studies using THZ1 to inhibit transcription in HGSOC cells, suggesting early down-regulation of these genes may be responsible for cytotoxic effects of THZ1 on HGSOC models. Identification of MTFs across 34 tumor types and 140 subtypes, especially for those with limited understanding of transcriptional drivers paves the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

scholarly journals Comprehensive Analysis of Indels in Whole-genome Microsatellite Regions and Microsatellite Instability across 21 Cancer Types

2018 ◽  
Author(s):  
Akihiro Fujimoto ◽  
Masashi Fujita ◽  
Takanori Hasegawa ◽  
Jing Hao Wong ◽  
Kazuhiro Maejima ◽  
...  
Keyword(s):  
Microsatellite Instability ◽  
Mismatch Repair ◽  
High Sensitivity ◽  
Replication Timing ◽  
Mutation Rates ◽  
Structural Variations ◽  
Dna Mismatch ◽  
Germline Variation ◽  
Microsatellite Mutation Rate ◽  
Pan Cancer

AbstractMicrosatellites are repeats of 1-6bp units and ∼10 million microsatellites have been identified across the human genome. Microsatellites are vulnerable to DNA mismatch errors, and have thus been used to detect cancers with mismatch repair deficiency. To reveal the mutational landscape of the microsatellite repeat regions at the genome level, we analyzed approximately 20.1 billion microsatellites in 2,717 whole genomes of pan-cancer samples across 21 tissue types. Firstly, we developed a new insertion and deletion caller (MIMcall) that takes into consideration the error patterns of different types of microsatellites. Among the 2,717 pan-cancer samples, our analysis identified 31 samples, including colorectal, uterus, and stomach cancers, with higher microsatellite mutation rate (≥ 0.03), which we defined as microsatellite instability (MSI) cancers in genome-wide level. Next, we found 20 highly-mutated microsatellites that can be used to detect MSI cancers with high sensitivity. Third, we found that replication timing and DNA shape were significantly associated with mutation rates of the microsatellites. Analysis of germline variation of the microsatellites suggested that the amount of germline variations and somatic mutation rates were correlated. Lastly, analysis of mutations in mismatch repair genes showed that somatic SNVs and short indels had larger functional impact than germline mutations and structural variations. Our analysis provides a comprehensive picture of mutations in the microsatellite regions, and reveals possible causes of mutations, as well as provides a useful marker set for MSI detection.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

2021 ◽  
pp. 1-10
Author(s):  
Zoe Guan ◽  
Ronglai Shen ◽  
Colin B. Begg
Keyword(s):  
Rare Variant ◽  
Rare Variants ◽  
Association Studies ◽  
The Cancer Genome Atlas ◽  
Considerable Proportion ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Risk Variants ◽  
Cancer Types ◽  
Pan Cancer

<b><i>Background:</i></b> Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The “rare variant hypothesis” proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. <b><i>Objectives:</i></b> In this study, we investigated associations between rare variants and 14 cancer types. <b><i>Methods:</i></b> We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). <b><i>Results:</i></b> We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). <b><i>Conclusions:</i></b> Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

scholarly journals The Gasdermin E gene Potential as a Pan-Cancer Biomarker, While Discriminating between Different Tumor Types

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1810 ◽  
Author(s):  
Joe Ibrahim ◽  
Ken Op de Beeck ◽  
Erik Fransen ◽  
Marc Peeters ◽  
Guy Van Camp
Keyword(s):  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
E Gene ◽  
Normal Tissues ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Cancer Setting ◽  
Tumor Types ◽  
Methylation Patterns ◽  
Pan Cancer

Due to the elevated rates of incidence and mortality of cancer, early and accurate detection is crucial for achieving optimal treatment. Molecular biomarkers remain important screening and detection tools, especially in light of novel blood-based assays. DNA methylation in cancer has been linked to tumorigenesis, but its value as a biomarker has not been fully explored. In this study, we have investigated the methylation patterns of the Gasdermin E gene across 14 different tumor types using The Cancer Genome Atlas (TCGA) methylation data (N = 6502). We were able to identify six CpG sites that could effectively distinguish tumors from normal samples in a pan-cancer setting (AUC = 0.86). This combination of pan-cancer biomarkers was validated in six independent datasets (AUC = 0.84–0.97). Moreover, we tested 74,613 different combinations of six CpG probes, where we identified tumor-specific signatures that could differentiate one tumor type versus all the others (AUC = 0.79–0.98). In all, methylation patterns exhibited great variation between cancer and normal tissues, but were also tumor specific. Our analyses highlight that a Gasdermin E methylation biomarker assay, not only has the potential for being a methylation-specific pan-cancer detection marker, but it also possesses the capacity to discriminate between different types of tumors.

Distribution of ETV6-NTRK3 translocations across neoplasms identified from the Mitelman Database.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23141-e23141
Author(s):  
Juan Carlos Malpartida ◽  
Eric Vick ◽  
Noah Hunter Richardson ◽  
Kruti Patel ◽  
Matthew K Stein ◽  
...  
Keyword(s):  
Ductal Carcinoma ◽  
Tumor Type ◽  
Invasive Adenocarcinoma ◽  
Data Set ◽  
Oncogenic Potential ◽  
Multiple Tumor ◽  
Cancer Types ◽  
Mitelman Database ◽  
Tumor Types ◽  
Tumor Profiling

e23141 Background: Discovered as a novel aberration in congenital fibrosarcoma (CF), the ETV6-NTRK3 translocation (EN) confers oncogenic potential and is inhibited by crizotinib. The present study aims to survey the scope of neoplasms that harbor EN across tumor types. Methods: Utilizing the National Cancer Institute’s Mitelman Database (MD) of Chromosome Aberrations and Gene Fusions patients (pts) were identified with EN and categorized based on tumor type, subtype and incidence. Cancer pts who received tumor profiling with Caris were also surveyed for EN. Results: 47 pts with EN across 12 cancer types were extracted from the MD and had median age of 0.17 years (7 unreported); 38% male; 51% acquired malignancies, 49% congenital; 62% cases were pediatric, 23% adult and 15% unknown. 0/204 pts with Caris tumor profiling were found to have an EN. Cancers with the highest number of EN were: 15 (31.9% EN data set) congenital mesoblastic nephromas (CMN), 10 (21.3%) CF, 7 (14.9%) breast carcinoma (BC; 6 secretory ductal carcinoma (SD) and 1 invasive adenocarcinoma (IA)) and 3 (6.4%) colorectal carcinoma (CRC). EN were found in 8 other malignancies (Table 1). Cancer types with the highest incidence of EN+ cases in the MD were gastrointestinal stromal tumor (GIST; 100%), CMN (75%) and CF (23.3%). Conclusions: These results further our understanding of the distribution of ETV6-NTRK3 translocations in multiple tumor types across the age spectrum and suggest that pts with CMN, CF, BC and CRC requiring high order therapy should be considered for NTRK3-based treatment. [Table: see text]

Genomic and immune infiltration differences between MSI and MSS GI tumors.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 528-528
Author(s):  
Yulia Newton ◽  
Justin Golovato ◽  
Iain Beehuat Tan ◽  
Justina Yick Ching Lam ◽  
Guo Yu ◽  
...  
Keyword(s):  
Microsatellite Instability ◽  
Variant Calling ◽  
Multiple Cancer ◽  
Immune Signaling ◽  
Gene Markers ◽  
Immune Infiltration ◽  
Dna Mismatch ◽  
Cellular Integrity ◽  
Type Gene ◽  
Tumor Types

528 Background: Dysregulation of DNA mismatch repair pathway can lead to microsatellite instability in many GI tumors, and microsatellite instability is an important diagnostic and prognostic marker. Microsatellite instable (MSI) tumors comprise about 15% of colorectal malignancies and can be found in other gastrointestinal (GI) tumor types. We present results of analysis of genomic and immune infiltration differences between MSI and microsatellite stable (MSS) GI tumors spanning multiple cancer types. Methods: A total of 521 GI patients with deep whole exome sequencing (WES) of tumor and blood samples, and whole transcriptomic sequencing (RNA-Seq) (∼200M reads per tumor) were available for this analysis from a commercial database. Variant calling was performed through joint probabilistic analysis of tumor and normal DNA reads, with germline status of variants being determined by heterozygous or homozygous alternate allele fraction in the germline sample. Results: Gene expression and pathway analysis found significantly higher immune signaling in MSI cohort and higher metabolic signaling in MSS cohort. We also found upregulation of structural cellular integrity pathways in MSI tumors. Per-sample deconvolution of immune infiltration using cell type gene markers shows some MSI samples with high CD8 T-cells. Co-expression analysis of checkpoint and TME genes shows higher correlation of FOXP3 and CTLA4 in the MSS cohort compared to the MSI samples, whereas correlation between FOXP3 and PDL1 is decreased. TIM3, LAG3, and OX40 are significantly more expressed in MSI samples than MSS samples. Within the subset of colorectal tumors, additional checkpoints are significantly differentially overexpressed in MSI malignancies. 50 somatic variants are significantly differential in MSI tumors. Conclusions: MSI tumors demonstrably exhibit higher immune signaling, with many immune and checkpoint markers expressed at higher levels in MSI tumors. Some cellular integrity pathways also appear to be up in MSI cohort. A number of potentially important somatic variants are associated with MSI samples.

scholarly journals Bayesian inference of cancer driver genes using signatures of positive selection

2017 ◽  
Author(s):  
Luis Zapata ◽  
Hana Susak ◽  
Oliver Drechsel ◽  
Marc R. Friedländer ◽  
Xavier Estivill ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Large Fraction ◽  
Driver Gene ◽  
Tumor Type ◽  
Sequencing Data ◽  
Cancer Etiology ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Types ◽  
Cancer Cell Fraction

AbstractTumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian inference model for driver prediction. In an in-depth benchmark, we demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

scholarly journals CYP1B1-AS1 Is a Novel Biomarker in Glioblastoma by Comprehensive Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tao Ye ◽  
Lan-lan Li ◽  
Xue-mei Peng ◽  
Qin Li
Keyword(s):  
Mutual Effect ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Sequencing Data ◽  
Molecular Biomarker ◽  
Kaplan Meier ◽  
Cancer Types ◽  
Signal Path ◽  
Tumor Types ◽  
Spearman’S Correlation

Objective. Growing evidence shows that enhancer RNAs (eRNAs) are pivotal for tumor progression. In this research, our team aimed to identify the survival-related eRNAs and further explore their potential function in glioblastoma (GBM). Methods. RNA-sequencing data in 31 tumor types were acquired from TCGA datasets. The survival-related eRNAs were identified by the use of Kaplan-Meier survival analyses and Spearman’s correlation analyses. KEGG pathway enrichment analysis was completed to investigate the underlying signal paths of the critical eRNA. Pancancer assays were applied to explore the association between CYP1B1-AS1 and CYP1B1. Results. We identified 74 survival-related eRNAs and focused on CYP1B1-AS1 which displayed the greatest cor value. CYP1B1 was identified as a regulatory target of CYP1B1-AS1. KEGG analyses suggested that CYP1B1-AS1 might play an essential role through CK-CKR mutual effect, complement and coagulation cascades, TNF signal path, and JAK-STAT signal path. The pancancer verification outcomes revealed that CYP1B1-AS1 was related to survival in 4 cancers, i.e., LIHC, KIRP, KICH, and KIRC. Association was discovered between CYP1B1-AS1 and the targeted gene, CYP1B1, in 29 cancer types. Conclusion. The outcomes herein provided the first evidence that overexpression of CYP1B1-AS1 might be a potential molecular biomarker for predicting the prognosis of patients with GBM.

scholarly journals Deep Learning Based Tumor Type Classification Using Gene Expression Data

2018 ◽  
Author(s):  
Boyu Lyu ◽  
Anamul Haque
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tumor Type ◽  
Differential Analysis ◽  
Rna Seq ◽  
Heat Map ◽  
Standard Practices ◽  
Tumor Types ◽  
Pan Cancer

ABSTRACTDifferential analysis occupies the most significant portion of the standard practices of RNA-Seq analysis. However, the conventional method is matching the tumor samples to the normal samples, which are both from the same tumor type. The output using such method would fail in differentiating tumor types because it lacks the knowledge from other tumor types. Pan-Cancer Atlas provides us with abundant information on 33 prevalent tumor types which could be used as prior knowledge to generate tumor-specific biomarkers. In this paper, we embedded the high dimensional RNA-Seq data into 2-D images and used a convolutional neural network to make classification of the 33 tumor types. The final accuracy we got was 95.59%, higher than another paper applying GA/KNN method on the same dataset. Based on the idea of Guided Grad Cam, as to each class, we generated significance heat-map for all the genes. By doing functional analysis on the genes with high intensities in the heat-maps, we validated that these top genes are related to tumor-specific pathways, and some of them have already been used as biomarkers, which proved the effectiveness of our method. As far as we know, we are the first to apply convolutional neural network on Pan-Cancer Atlas for classification, and we are also the first to match the significance of classification with the importance of genes. Our experiment results show that our method has a good performance and could also apply in other genomics data.

scholarly journals The effects of mutational processes and selection on driver mutations across cancer types

2017 ◽  
Author(s):  
Daniel Temko ◽  
Ian PM Tomlinson ◽  
Simone Severini ◽  
Benjamin Schuster-Böckler ◽  
Trevor A Graham
Keyword(s):  
Driver Mutations ◽  
Sequencing Data ◽  
Epidemiological Evidence ◽  
Dna Mismatch ◽  
Cancer Types ◽  
Key Driver ◽  
Independent Effects ◽  
Mutational Processes ◽  
Mutation And Selection

ABSTRACTEpidemiological evidence has long associated environmental mutagens with increased cancer risk. However, links between specific mutation-causing processes and the acquisition of individual driver mutations have remained obscure. Here we have used public cancer sequencing data to infer the independent effects of mutation and selection on driver mutation complement. First, we detect associations between a range of mutational processes, including those linked to smoking, ageing, APOBEC and DNA mismatch repair (MMR) and the presence of key driver mutations across cancer types. Second, we quantify differential selection between well-known alternative driver mutations, including differences in selection between distinct mutant residues in the same gene. These results show that while mutational processes play a large role in determining which driver mutations are present in a cancer, the role of selection frequently dominates.

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