scholarly journals Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints after Treatment of Cell Lines with Demethylation

2016 ◽  
Vol 15 ◽  
pp. CIN.S31809
Author(s):  
Manama El Baroudi ◽  
Caterina Cinti ◽  
Enrico Capobianco
Keyword(s):  
Immune System ◽  
Cell Lines ◽  
Network Inference ◽  
Cancer Hallmarks ◽  
Regulation Mechanisms ◽  
Cancer Types ◽  
Synergistic Modulation ◽  
Cancer Immunotherapies ◽  
Post Transcriptional Regulation ◽  
Pan Cancer

Pan-cancer studies are particularly relevant not only for addressing the complexity of the inherently observed heterogeneity but also for identifying clinically relevant features that may be common to the cancer types. Immune system regulations usually reveal synergistic modulation with other cancer mechanisms and in combination provide insights on possible advances in cancer immunotherapies. Network inference is a powerful approach to decipher pan-cancer systems dynamics. The methodology proposed in this study elucidates the impacts of epigenetic treatment on the drivers of complex pan-cancer regulation circuits involving cell lines of five cancer types. These patterns were observed from differential gene expression measurements following demethylation with 5-azacytidine. Networks were built to establish associations of phenotypes at molecular level with cancer hallmarks through both transcriptional and post-transcriptional regulation mechanisms. The most prominent feature that emerges from our integrative network maps, linking pathway landscapes to disease and drug-target associations, refers primarily to a mosaic of immune-system crosslinked influences. Therefore, characteristics initially evidenced in single cancer maps become motifs well summarized by network cores and fingerprints.

scholarly journals Immuno-Oncology Integrative Networks: Elucidating the Influences of Osteosarcoma Phenotypes

2017 ◽  
Vol 16 ◽  
pp. 117693511772169 ◽  
Author(s):  
Ankush Sharma ◽  
Enrico Capobianco
Keyword(s):  
Transcription Factor ◽  
Immune System ◽  
Cell Lines ◽  
Network Inference ◽  
Differentially Expressed Gene ◽  
Regulatory Activity ◽  
Coexpression Networks ◽  
Pan Cancer

In vivo and in vitro functional phenotyping characterization was recently obtained with reference to an experimental pan-cancer study of 22 osteosarcoma (OS) cell lines. Here, differentially expressed gene (DEG) profiles were recomputed from the publicly available data to conduct network inference on the immune system regulatory activity across the characterized OS phenotypes. Based on such DEG profiles, and for each phenotype that was analyzed, we obtained coexpression networks and bio-annotations for them. Then, we described the immune-modulated influences in phenotype-specific networks’ integrating pathway, transcription factor, and microRNA regulations. Overall, this approach seems suitable for representing heterogeneity in OS tumorigenesis.

scholarly journals Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

2021 ◽  
Vol 17 (2) ◽  
pp. e1008720
Author(s):  
John P. Lloyd ◽  
Matthew B. Soellner ◽  
Sofia D. Merajver ◽  
Jun Z. Li
Keyword(s):  
Sample Size ◽  
Cell Lines ◽  
Drug Response ◽  
Joint Analysis ◽  
Cancer Type ◽  
Cancer Prediction ◽  
Cancer Models ◽  
Cancer Types ◽  
Tissue Differences ◽  
Pan Cancer

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88–0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11–0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman’s ρ from a range of 0.43–0.62 to 0.30–0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

scholarly journals Epithelial Ovarian Cancer and the Immune System: Biology, Interactions, Challenges and Potential Advances for Immunotherapy

2020 ◽  
Vol 9 (9) ◽  
pp. 2967
Author(s):  
Anne M. Macpherson ◽  
Simon C. Barry ◽  
Carmela Ricciardelli ◽  
Martin K. Oehler
Keyword(s):  
Ovarian Cancer ◽  
Immune System ◽  
Epithelial Ovarian Cancer ◽  
Current Knowledge ◽  
Tumour Microenvironment ◽  
Tumour Immunology ◽  
The Us ◽  
Cancer Types ◽  
Cancer Immunotherapeutic ◽  
Cancer Immunotherapies

Recent advances in the understanding of immune function and the interactions with tumour cells have led to the development of various cancer immunotherapies and strategies for specific cancer types. However, despite some stunning successes with some malignancies such as melanomas and lung cancer, most patients receive little or no benefit from immunotherapy, which has been attributed to the tumour microenvironment and immune evasion. Although the US Food and Drug Administration have approved immunotherapies for some cancers, to date, only the anti-angiogenic antibody bevacizumab is approved for the treatment of epithelial ovarian cancer. Immunotherapeutic strategies for ovarian cancer are still under development and being tested in numerous clinical trials. A detailed understanding of the interactions between cancer and the immune system is vital for optimisation of immunotherapies either alone or when combined with chemotherapy and other therapies. This article, in two main parts, provides an overview of: (1) components of the normal immune system and current knowledge regarding tumour immunology, biology and their interactions; (2) strategies, and targets, together with challenges and potential innovative approaches for cancer immunotherapy, with attention given to epithelial ovarian cancer.

scholarly journals Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

2019 ◽  
Author(s):  
John P. Lloyd ◽  
Matthew Soellner ◽  
Sofia D. Merajver ◽  
Jun Z. Li
Keyword(s):  
Sample Size ◽  
Cell Lines ◽  
Drug Response ◽  
Joint Analysis ◽  
Cancer Type ◽  
Cancer Prediction ◽  
Cancer Models ◽  
Cancer Types ◽  
Tissue Differences ◽  
Pan Cancer

ABSTRACTIncreased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines with MEK inhibitor (MEKi) response and RNA, SNP, and CNV data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ=0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ=0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as we estimate that exclusion of between-tissue signals leads to a 22% decrease in performance metrics. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that the higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

scholarly journals The Functional Characterization of Epigenetically Related lncRNAs Involved in Dysregulated CeRNA–CeRNA Networks Across Eight Cancer Types

2021 ◽  
Vol 9 ◽  
Author(s):  
Dahua Xu ◽  
Liqiang Wang ◽  
Sainan Pang ◽  
Meng Cao ◽  
Wenxiang Wang ◽  
...  
Keyword(s):  
Functional Characterization ◽  
Cancer Hallmarks ◽  
Diagnosis And Prognosis ◽  
Competing Endogenous Rnas ◽  
Cancer Types ◽  
Cancer Outcome ◽  
The Individual ◽  
Molecular Therapeutic ◽  
Molecular Therapeutic Targets ◽  
Pan Cancer

Numerous studies have demonstrated that lncRNAs could compete with other RNAs to bind miRNAs, as competing endogenous RNAs (ceRNAs), to regulate each other. On the other hand, ceRNAs were found to be recurrently dysregulated in cancer status. However, limited studies considered the upstream epigenetic regulatory factors that disrupted the normal competing mechanism. In the present study, we constructed the lncRNA-associated dysregulated ceRNA networks across eight cancer types. lncRNAs in the individual dysregulated network and pan-cancer core dysregulated ceRNA subnetwork were found to play more important roles than mRNAs. Integrating lncRNA methylation profiles, we identified 49 epigenetically related (ER) lncRNAs involved in the dysregulated ceRNA networks, including 18 epigenetically activated (EA) lncRNAs, 18 epigenetically silenced (ES) lncRNAs, and 13 rewired ER lncRNAs across eight cancer types. Furthermore, we evaluated the epigenetic regulating patterns of these lncRNAs and screened nine pan-cancer ER lncRNAs (six EA and three ES lncRNAs). The nine lncRNAs were found to regulate the cancer hallmarks by competing with mRNAs. Moreover, we found that integrating the expression and methylation profiles of the nine lncRNAs could predict cancer incidence in eight cancer types robustly and the cancer outcome of several cancer types. These results provide an improved understanding of methylation regulation to ceRNA and offer novel potential molecular therapeutic targets for the diagnosis and prognosis across different cancer types.

scholarly journals Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

2020 ◽  
Vol 48 (5) ◽  
pp. 2287-2302 ◽  
Author(s):  
Zishan Wang ◽  
Jiaqi Yin ◽  
Weiwei Zhou ◽  
Jing Bai ◽  
Yunjin Xie ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Transcriptional Regulation ◽  
Human Cancer ◽  
Network Module ◽  
Regulatory Activity ◽  
Valuable Resource ◽  
Cancer Hallmarks ◽  
Transcriptional Dysregulation ◽  
Cancer Types ◽  
Pan Cancer

Abstract Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.

scholarly journals Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

2021 ◽  
Vol 9 ◽  
Author(s):  
ShiJian Ding ◽  
Hao Li ◽  
Yu-Hang Zhang ◽  
XianChao Zhou ◽  
KaiYan Feng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Cell Line ◽  
Cancer Patients ◽  
Cell Lines ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Biomarkers ◽  
Cancer Types ◽  
Pan Cancer

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.

scholarly journals Pan-Cancer Genome-Wide DNA Methylation Analyses Revealed That Hypermethylation Influences 3D Architecture and Gene Expression Dysregulation in HOXA Locus During Carcinogenesis of Cancers

2021 ◽  
Vol 9 ◽  
Author(s):  
Gang Liu ◽  
Zhenhao Liu ◽  
Xiaomeng Sun ◽  
Xiaoqiong Xia ◽  
Yunhe Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cell Lines ◽  
Dna Interaction ◽  
Gene Expression Omnibus ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Topological Domains ◽  
Cancer Types ◽  
Pan Cancer

DNA methylation dysregulation during carcinogenesis has been widely discussed in recent years. However, the pan-cancer DNA methylation biomarkers and corresponding biological mechanisms were seldom investigated. We identified differentially methylated sites and regions from 5,056 The Cancer Genome Atlas (TCGA) samples across 10 cancer types and then validated the findings using 48 manually annotated datasets consisting of 3,394 samples across nine cancer types from Gene Expression Omnibus (GEO). All samples’ DNA methylation profile was evaluated with Illumina 450K microarray to narrow down the batch effect. Nine regions were identified as commonly differentially methylated regions across cancers in TCGA and GEO cohorts. Among these regions, a DNA fragment consisting of ∼1,400 bp detected inside the HOXA locus instead of the boundary may relate to the co-expression attenuation of genes inside the locus during carcinogenesis. We further analyzed the 3D DNA interaction profile by the publicly accessible Hi-C database. Consistently, the HOXA locus in normal cell lines compromised isolated topological domains while merging to the domain nearby in cancer cell lines. In conclusion, the dysregulation of the HOXA locus provides a novel insight into pan-cancer carcinogenesis.

scholarly journals Landscape of Chimeric RNAs in Non-Cancerous Cells

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 466
Author(s):  
Chen Chen ◽  
Samuel Haddox ◽  
Yue Tang ◽  
Fujun Qin ◽  
Hui Li
Keyword(s):  
Cancer Cells ◽  
Cell Lines ◽  
Drug Targets ◽  
Neoplastic Cell ◽  
Multiple Cancer ◽  
Chimeric Rnas ◽  
Healthy Donors ◽  
Cancer Types ◽  
Chimeric Rna ◽  
Cancerous Cells

Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.

scholarly journals Characterization of the Survival Influential Genes in Carcinogenesis

2021 ◽  
Vol 22 (9) ◽  
pp. 4384
Author(s):  
Divya Sahu ◽  
Yu-Lin Chang ◽  
Yin-Chen Lin ◽  
Chen-Ching Lin
Keyword(s):  
Cell Cycle ◽  
Cox Regression ◽  
Monte Carlo Algorithm ◽  
Functional Modules ◽  
Protective Genes ◽  
Cancer Types ◽  
Underlying Mechanisms ◽  
Patient Prognosis ◽  
Pan Cancer

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

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