scholarly journals Mutually Antagonistic Protein Pairs of Cancer

2021 ◽  
Author(s):  
Ertugrul Dalgic
Keyword(s):  
Differential Expression ◽  
Large Scale ◽  
Expression Patterns ◽  
The Other ◽  
Cancer Type ◽  
Mutual Inhibition ◽  
Extensive Analysis ◽  
Cancer Types ◽  
Specific Antagonist ◽  
Potential Cancer

Switch-like behavior of tumorigenesis could be governed by antagonistic gene and protein pairs with mutual inhibition. Unlike extensive analysis of gene expression, search for protein level antagonistic pairs has been limited. Here, potential cancer type specific antagonist protein pairs with mutual inhibition were obtained from large scale datasets. Cancer samples or cancer types were compared to retrieve potential protein pairs with contrasting differential expression patterns. Analysis of two different protein expression datasets showed that a few proteins participate in most of the mutually antagonistic relationships. Some proteins with highly antagonistic profile were identified, which could not be attained from a differential expression or a correlation based analysis. The antagonistic protein pairs are sparsely connected by molecular interactions. Glioma, melanoma, and cervical cancer, are more frequently associated with antagonistic proteins than most of the other cancer types. Integrative analysis of mutually antagonist protein pairs contributes to our understanding of systems level changes of cancer.

scholarly journals Integrative analyses of the RNA modification machinery reveal tissue- and cancer-specific signatures

2019 ◽  
Author(s):  
Oguzhan Begik ◽  
Morghan C. Lucas ◽  
Huanle Liu ◽  
Jose Miguel Ramirez ◽  
John S. Mattick ◽  
...  
Keyword(s):  
Cancer Research ◽  
Expression Patterns ◽  
Rna Modification ◽  
Cancer Type ◽  
Evolutionary Analysis ◽  
Rna Modifications ◽  
Human Samples ◽  
Mammalian Tissues ◽  
Normal Human ◽  
Cancer Types

ABSTRACTBackgroundRNA modifications play central roles in cellular fate and differentiation. These features have placed the epitranscriptome in the forefront of developmental biology and cancer research. However, the machinery responsible for placing, removing and recognizing more than 170 RNA modifications remains largely uncharacterized and poorly annotated, and we currently lack integrative studies that identify which RNA modification–related proteins (RMPs) may be dysregulated in each cancer type.ResultsHere we have performed a comprehensive annotation and evolutionary analysis of human RMPs as well as an integrative analysis of their expression patterns across 32 tissues, 10 species and 13,358 paired tumor-normal human samples. Our analysis reveals an unanticipated heterogeneity of RMP expression patterns across mammalian tissues, with a vast proportion of duplicated enzymes displaying testis-specific expression, suggesting a key role for RNA modifications in sperm formation and possibly intergenerational inheritance. Moreover, through the analysis of paired tumor-normal human samples we uncover many RMPs that are dysregulated in various types of cancer, and whose expression levels are predictive of cancer progression. Surprisingly, we find that several commonly studied RNA modification enzymes such as METTL3 or FTO, are not significantly up-regulated in most cancer types, once the sample is properly scaled and normalized to the full dataset, whereas several less-characterized RMPs, such as LAGE3 and HENMT1, are dysregulated in many cancers.ConclusionsOur analyses reveal an unanticipated heterogeneity in the expression patterns of RMPs across mammalian tissues, and uncover a large proportion of dysregulated RMPs in multiple cancer types. We provide novel targets for future cancer research studies targeting the human epitranscriptome, as well as foundations to understand cell type-specific behaviours that are orchestrated by RNA modifications.

scholarly journals Mutational interactions define novel cancer subgroups

2017 ◽  
Author(s):  
Jack Kuipers ◽  
Thomas Thurnherr ◽  
Giusi Moffa ◽  
Polina Suter ◽  
Jonas Behr ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Cancer Progression ◽  
Large Scale ◽  
De Novo ◽  
Drug Repositioning ◽  
Tissue Type ◽  
Survival Prediction ◽  
Cancer Type ◽  
Cancer Data ◽  
Cancer Types

Large-scale genomic data can help to uncover the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues of origin highlights differences and similarities which can guide drug repositioning as well as the design of targeted and precise treatments. Here, we developed an improved Bayesian network model for tumour mutational profiles and applied it to 8,198 patient samples across 22 cancer types from TCGA. For each cancer type, we identified the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we found genes which interact both within and across cancer types. To detach cancer classification from the tissue type we performed de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We found 22 novel clusters which significantly improved survival prediction beyond clinical and histopathological information. The models highlight key gene interactions for each cluster that can be used for genomic stratification in clinical trials and for identifying drug targets within strata.

scholarly journals Recessive effects in cancer predisposition exposed by genome-wide and proteome-wide association studies

2020 ◽  
Author(s):  
Nadav Brandes ◽  
Nathan Linial ◽  
Michal Linial
Keyword(s):  
Cancer Risk ◽  
Large Scale ◽  
Association Studies ◽  
Cancer Predisposition ◽  
Small Scale ◽  
Cancer Type ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Cancer Types ◽  
Genomic Regions

AbstractThe characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Current attempts to detect cancer predisposition genomic regions are typically based on small-scale familial studies or genome-wide association studies (GWAS) over dedicated case-control cohorts. In this study, we utilized the UK Biobank as a large-scale prospective cohort to conduct a comprehensive analysis of cancer predisposition using both GWAS and proteome-wide association study (PWAS), a method that highlights genetic associations mediated by functional alterations to protein-coding genes. We discovered 137 unique genomic loci implicated with cancer risk in the white British population across nine cancer types and pan-cancer. While most of these genomic regions are supported by external evidence, our results highlight novel loci as well. We performed a comparative analysis of cancer predisposition between cancer types, finding that most of the implicated regions are cancer-type specific. We further analyzed the role of recessive genetic effects in cancer predisposition. We found that 30 of the 137 cancer regions were recovered only by a recessive model, highlighting the importance of recessive inheritance outside of familial studies. Finally, we show that many of the cancer associations exert substantial cancer risk in the studied cohort, suggesting their clinical relevance.

scholarly journals Quantitative Analysis of Differential Expression of HOX Genes in Multiple Cancers

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1572
Author(s):  
Orit Adato ◽  
Yaron Orenstein ◽  
Juri Kopolovic ◽  
Tamar Juven-Gershon ◽  
Ron Unger
Keyword(s):  
Gene Expression ◽  
Quantitative Analysis ◽  
Differential Expression ◽  
Survival Data ◽  
Hox Genes ◽  
Expression Patterns ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Hox Gene ◽  
Cancer Types

Transcription factors encoded by Homeobox (HOX) genes play numerous key functions during early embryonic development and differentiation. Multiple reports have shown that mis-regulation of HOX gene expression plays key roles in the development of cancers. Their expression levels in cancers tend to differ based on tissue and tumor type. Here, we performed a comprehensive analysis comparing HOX gene expression in different cancer types, obtained from The Cancer Genome Atlas (TCGA), with matched healthy tissues, obtained from Genotype-Tissue Expression (GTEx). We identified and quantified differential expression patterns that confirmed previously identified expression changes and highlighted new differential expression signatures. We discovered differential expression patterns that are in line with patient survival data. This comprehensive and quantitative analysis provides a global picture of HOX genes’ differential expression patterns in different cancer types.

Integrating molecular profiling into cancer treatment decision making: Experience with over 35,000 cases.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11001-11001 ◽  
Author(s):  
Zoran Gatalica ◽  
Sherri Millis ◽  
Sting Chen ◽  
Gargi Dan Basu ◽  
Wenhsiang Wen ◽  
...  
Keyword(s):  
Targeted Therapy ◽  
Protein Expression ◽  
Kras Mutation ◽  
Expression Patterns ◽  
Soft Tissue Sarcomas ◽  
Molecular Profiling ◽  
Cancer Type ◽  
Rare Cancer ◽  
Her2 Amplification ◽  
Cancer Types

11001 Background: Molecular profiling of both common and rare cancer types provides for the identification of actionable targets for chemotherapy with many unexpected associations. Methods: Caris Life Sciences database of >35,000 profiled cancers was reviewed for well-established driver gene mutations and copy number alterations, and protein expression patterns that are relevant for selection of targeted therapy. Based on the published literature, these tumor characteristics were then associated with potential benefit or no benefit to the specific therapeutic agents. All relevant published studies were evaluated using the USPSTF grading scheme for study design and validity. Assay methodologies included sequencing (Sanger, pyrosequencing), PCR, FISH, CISH, and immunohistochemistry. Results: All common malignancies (10 most common cancer types in men and women) and 10 rare cancer types were well represented (minimum of 100 cases in each individual cancer type). Well established driver mutations and protein expression in common cancers were all identified with expected frequencies (e.g. HER2 amplification in breast, PIK3CA mutations in ER+ breast cancer, EGFR mutations in NSCLC, etc.). Importantly, unexpected new and potentially actionable targets were identified in common (e.g., 6.7% HER2 amplification in NSCLC, 1.6% KRAS mutation in prostatic adenocarcinoma) and rare cancers (e.g., 8.3% ALK alteration in soft tissue sarcomas, 10.5% c-MET and 26.4% EGFR gene amplification in melanomas, 16.3% KRAS mutation in cholangiocarcinomas, 10% AR expression in STS), as well in cancers of unknown primary site (approximately 4% of all tested cases). Conclusions: This review of the large referral cancer profiling database provided an unparalleled insight in the distribution of common and rare genetic and protein alterations with direct and potential treatment implications. Numerous targets were discovered that had a potential to be treated by the conventional chemotherapy as well as targeted therapy not usually considered for the cancer type. Comparison between an individual patient tumor profile and database for the matched cancer type provides additional level of support for targeted treatment choices.

scholarly journals A Pan-Cancer Study of KMT2 Family as Therapeutic Targets in Cancer

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Jiamin Zhu ◽  
Zhili Liu ◽  
Xiao Liang ◽  
Lu Wang ◽  
Dan Wu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Tumor Microenvironment ◽  
Patient Survival ◽  
Expression Patterns ◽  
Family Members ◽  
Cancer Type ◽  
Tumor Type ◽  
Histone Lysine ◽  
Cancer Types ◽  
Pan Cancer

Objective. Exome sequencing studies have shown that the histone-lysine N-methyltransferase 2 (KMT2) gene is one of the most commonly mutated genes in a range of human malignancies and is linked to some of the most common and deadly solid tumors. However, the connection between this gene family’s function and tumor type, immunological subtype, and molecular subtype dependency is still unknown. Methods. We examine the expression patterns of the histone-lysine N-methyltransferase 2 (KMT2) gene, as well as their relationship to patient survival. We also used a pan-cancer analysis to link their function to immunological subtypes, the tumor microenvironment, and treatment sensitivity. Results. Using the TCGA pan-cancer data, researchers looked at and examined KMT2 expression patterns and their links to patient survival and the tumor microenvironment in 33 cancer types. The expression of the KMT2 family changes significantly across and within cancer types, indicating significant inter- and intracancer heterogeneity. Patients’ overall survival was often linked to the expression of KMT2 family members. However, the direction of the link differed depending on the KMT2 isoform and cancer type studied. Notably, in all cancer types examined, nearly all KMT2 family members were substantially linked with overall survival in patients with renal clear cell carcinoma (KIRC). Furthermore, all KMT2 genes have a strong relationship with immune infiltrate subtypes, as well as varying degrees of stromal cell infiltration and tumor cell stemness. Finally, we discovered that higher expression of KMT2s, particularly KMT2F and KMT2G, was linked to greater chemotherapeutic sensitivity in several cell lines. Conclusions. The necessity to investigate each KMT2 member as a distinct entity inside each particular cancer type is highlighted by our comprehensive investigation of KMT2 gene expression and its relationship with immune infiltrates, tumor microenvironment, and cancer patient outcomes. Our research also confirmed the identification of KMT2 as a potential therapeutic target in cancer, but further laboratory testing is required.

scholarly journals cancerAlign: Stratifying tumors by unsupervised alignment across cancer types

2020 ◽  
Author(s):  
Bowen Gao ◽  
Yunan Luo ◽  
Jianzhu Ma ◽  
Sheng Wang
Keyword(s):  
Somatic Mutation ◽  
Large Scale ◽  
Cancer Genomics ◽  
Population Level ◽  
Substantial Improvement ◽  
Cancer Type ◽  
Consensus Clustering ◽  
Learning To Learn ◽  
Mutation Profile ◽  
Cancer Types

ABSTRACTTumor stratification, which aims at clustering tumors into biologically meaningful subtypes, is the key step towards personalized treatment. Large-scale profiled cancer genomics data enables us to develop computational methods for tumor stratification. However, most of the existing approaches only considered tumors from an individual cancer type during clustering, leading to the overlook of common patterns across cancer types and the vulnerability to the noise within that cancer type. To address these challenges, we proposed cancerAlign to map tumors of the target cancer type into latent spaces of other source cancer types. These tumors were then clustered in each latent space rather than the original space in order to exploit shared patterns across cancer types. Due to the lack of aligned tumor samples across cancer types, cancerAlign used adversarial learning to learn the mapping at the population level. It then used consensus clustering to integrate cluster labels from different source cancer types. We evaluated cancerAlign on 7,134 tumors spanning 24 cancer types from TCGA and observed substantial improvement on tumor stratification and cancer gene prioritization. We further revealed the transferability across cancer types, which reflected the similarity among them based on the somatic mutation profile. cancerAlign is an unsupervised approach that provides deeper insights into the heterogeneous and rapidly accumulating somatic mutation profile and can be also applied to other genome-scale molecular information.Availabilityhttps://github.com/bowen-gao/cancerAlign

scholarly journals Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

2018 ◽  
Author(s):  
Yu Hu ◽  
Hayley Dingerdissen ◽  
Samir Gupta ◽  
Robel Kahsay ◽  
Vijay Shanker ◽  
...  
Keyword(s):  
Differential Expression ◽  
Cancer Progression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Literature Mining ◽  
Wide Range ◽  
Cancer Types

AbstractA number of microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common expression patterns of abnormally expressed miRNAs and their potential roles in multiple cancer types have not yet been evaluated. To minimize the difference of patients, we collected miRNA sequencing data of 575 patients with tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA), and performed differential expression analysis using DESeq2 and edgeR. The results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) unique to one of the 14 cancers may affect patient survival rate, and 21 key SDEmiRNAs (nine overexpressed and 12 under-expressed) associated with at least eight cancers and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effect of these 21 SDEmiRNAs on cellular functions was evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets from literature mining with known differential expression profiles in cancers. It enables identification of their functional similarity in cell proliferation control across a wide range of cancers and to build common regulatory networks over cancer-related pathways. This is validated by construction of a regulatory network in PI3K pathway. This study provides evidence of the value of further analysis on SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

scholarly journals CHASMplus reveals the scope of somatic missense mutations driving human cancers

2018 ◽  
Author(s):  
Collin Tokheim ◽  
Rachel Karchin
Keyword(s):  
Missense Mutation ◽  
Large Scale ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Superior Performance ◽  
Driver Mutations ◽  
Cancer Type ◽  
Missense Mutations ◽  
Driver Genes ◽  
Cancer Types

SummaryLarge-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the gene level to the mutation level, because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here we present CHASMplus, a computational method, that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas, CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy.SignificanceMissense mutations are the most frequent mutation type in cancers and the most difficult to interpret. While many computational methods have been developed to predict whether genes are cancer drivers or whether missense mutations are generally deleterious or pathogenic, there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type, limiting adoption of computational missense mutation predictors in the clinic. Cancer patients are routinely sequenced with targeted panels of cancer driver genes, but such genes contain a mixture of driver and passenger missense mutations which differ by cancer type. A patient’s therapeutic response to drugs and optimal assignment to a clinical trial depends on both the specific mutation in the gene of interest and cancer type. We present a new machine learning method honed for each TCGA cancer type, and a resource for fast lookup of the cancer-specific driver propensity of every possible missense mutation in the human exome.

scholarly journals NEBULA: a fast negative binomial mixed model for differential expression and co-expression analyses of large-scale multi-subject single-cell data

2020 ◽  
Author(s):  
Liang He ◽  
Alexander M. Kulminski
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Large Scale ◽  
Mixed Model ◽  
Negative Binomial ◽  
Expression Patterns ◽  
Marker Genes ◽  
Data Set ◽  
Cell Data ◽  
Negative Binomial Mixed Model

AbstractThe growing availability of large-scale single-cell data revolutionizes our understanding of biological mechanisms at a finer resolution. In differential expression and co-expression analyses of multi-subject single-cell data, it is important to take into account both subject-level and cell-level overdispersions through negative binomial mixed models (NBMMs). However, the application of NBMMs to large-scale single-cell data is computationally demanding. In this work, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA)), which analytically solves the high-dimensional integral in the marginal likelihood instead of using the Laplace approximation. Our benchmarks show that NEBULA dramatically reduces the running time by orders of magnitude compared to existing tools. We showed that NEBULA controlled false positives in identifying marker genes, while a simple negative binomial model produced spurious associations. Leveraging NEBULA, we decomposed between-subject and within-subject overdispersions of an snRNA-seq data set in the frontal cortex comprising ∼80,000 cells from a cohort of 48 individuals for Alzheimer’s diseases (AD). We observed that subpopulations and known subject-level covariates contributed substantially to the overdispersions. We carried out cell-type-specific transcriptome-wide within-subject co-expression analysis of APOE. The results revealed that APOE was most co-expressed with multiple AD-related genes, including CLU and CST3 in astrocytes, TREM2 and C1q genes in microglia, and ITM2B, an inhibitor of the amyloid-beta peptide aggregation, in both cell types. We found that the co-expression patterns were different in APOE2+ and APOE4+ cells in microglia, which suggest an isoform-dependent regulatory role in the immune system through the complement system in microglia. NEBULA opens up a new avenue for the broad application of NBMMs in the analysis of large-scale multi-subject single-cell data.

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