scholarly journals Molecular Targeted Therapies: Time for a Paradigm Shift in Medulloblastoma Treatment?

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 333
Author(s):  
Lidia Gatto ◽  
Enrico Franceschi ◽  
Alicia Tosoni ◽  
Vincenzo Di Nunno ◽  
Stefania Bartolini ◽  
...  
Keyword(s):  
Randomized Clinical Trials ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Pediatric Population ◽  
Age At Onset ◽  
Gene Expression Profiles ◽  
Adult Medulloblastoma ◽  
Disease Biology ◽  
Rare Malignancy ◽  
Oncology Practice

Medulloblastoma is a rare malignancy of the posterior cranial fossa. Although until now considered a single disease, according to the current WHO classification, it is a heterogeneous tumor that comprises multiple molecularly defined subgroups, with distinct gene expression profiles, pathogenetic driver alterations, clinical behaviors and age at onset. Adult medulloblastoma, in particular, is considered a rarer “orphan” entity in neuro-oncology practice because while treatments have progressively evolved for the pediatric population, no practice-changing prospective, randomized clinical trials have been performed in adults. In this scenario, the toughest challenge is to transfer the advances in cancer genomics into new molecularly targeted therapeutics, to improve the prognosis of this neoplasm and the treatment-related toxicities. Herein, we focus on the recent advances in targeted therapy of medulloblastoma based on the new and deeper knowledge of disease biology.

Genomic Fabric Remodeling in Prostate Cancer

2021 ◽  
Author(s):  
Sanda Iacobas ◽  
Dumitru A. Iacobas
Keyword(s):  
Prostate Cancer ◽  
Normal Tissue ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mathematical Object ◽  
P53 Signaling ◽  
Large Populations ◽  
Genomic Characterization ◽  
Meta Analyses

Prostate cancer is a leading cause of death among men but its genomic characterization and best therapeutic strategy are still under debate. The Genomic Fabric Paradigm (GFP) considers the transcriptome as a multi-dimensional mathematical object subjected to a dynamic set of expression correlations among the genes. Here, GFP is applied to gene expression profiles of three (one primary, and two secondary) cancer nodules and the surrounding normal tissue from a surgically removed prostate tumor. GFP was used to determine the regulation and rewiring of the P53 signaling, apoptosis, prostate cancer and several other pathways involved in survival and proliferation of the cancer cells. Genes responsible for the block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy and sustained angiogenesis were found as differently regulated in the three cancer nodules with respect to the normal tissue. The analysis indicates that even histo-pathologically equally graded cancer nodules from the same tumor have substantially different transcriptomic organizations, raising legitimate questions about the validity of meta-analyses comparing large populations of healthy and cancer humans. The study suggests that GFP may provide a personalized alternative to the biomarkers’ approach of cancer genomics.

scholarly journals Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242780
Author(s):  
Houriiyah Tegally ◽  
Kevin H. Kensler ◽  
Zahra Mungloo-Dilmohamud ◽  
Anisah W. Ghoorah ◽  
Timothy R. Rebbeck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Driver Mutations ◽  
Literature Mining ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

scholarly journals Directin vivomapping of functional suppressors in glioblastoma genome

2017 ◽  
Author(s):  
Ryan D. Chow ◽  
Christopher D. Guzman ◽  
Guangchuan Wang ◽  
Florian Schmidt ◽  
Mark W. Youngblood ◽  
...  
Keyword(s):  
Zinc Finger Protein ◽  
Cancer Genomics ◽  
Molecular Mapping ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Clusters ◽  
Single Digit ◽  
Crispr Screen ◽  
Multiple Challenges

AbstractGlioblastoma (GBM) is one of the deadliest cancers, with limited effective treatments and single-digit five-year survival1-7. A causative understanding of genetic factors that regulate GBM formation is of central importance8-19. However, a global, quantitative and functional understanding of gliomagenesis in the native brain environment has been lacking due to multiple challenges. Here, we developed an adeno-associated virus (AAV) mediated autochthonous CRISPR screen and directly mapped functional suppressors in the GBM genome. Stereotaxic delivery of an AAV library targeting significantly mutated genes into fully immunocompetent conditional Cas9 mice robustly led to gliomagenesis, resulting in tumors that recapitulate features of human GBM. Targeted capture sequencing revealed deep mutational profiles with diverse patterns across mice, uncoveringin vivoroles of previously uncharacterized factors in GBM such as immune regulatorB2m,zinc finger proteinZc3h13,transcription repressorCic,epigenetic regulatorsMll2/3andArid1b,alongside canonical tumor suppressorsNf1andPten. Comparative cancer genomics showed that the mutation frequencies across all genes tested in mice significantly correlate with those in human from two independent patient cohorts. Co-mutation analysis identified frequently co-occurring driver combinations, which were validated using AAV minipools, such asMll2, B2m-Nf1,Mll3-Nf1andZc3h13-Rb1. Distinct fromNf1-oncotype tumors,Rb1-oncotype tumors exhibit undifferentiated histopathology phenotype and aberrant activation of developmental reprogramming signatures such asHomeoboxgene clusters. The secondary addition ofZc3h13orPtenmutations drastically altered the gene expression profiles ofRb1mutants and rendered them more resistant to the GBM chemotherapeutic temozolomide. Our study provides a systematic functional landscape of GBM suppressors directlyin vivo, opening new paths for high-throughput molecular mapping and cancer phenotyping.

scholarly journals DNA Methylation Profiling of Myeloma Trial Patients Reveals Specific Epigenetic Changes Associated with Outcome

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 804-804
Author(s):  
David C Johnson ◽  
Dil B Begum ◽  
Sidra Ellis ◽  
Amy L Sherborne ◽  
Amy Price ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Methylated Regions ◽  
Disease Biology ◽  
High Risk Disease ◽  
Pathway Analyses ◽  
Support Research

Abstract Introduction Epigenetic dysregulation is a hallmark of cancer and has significant impact on disease biology. The epigenetic structure of myeloma is heterogeneous and we previously demonstrated that gene specific DNA methylation changes are associated with outcome, using low-resolution arrays. We now performed a high-resolution genome wide DNA methylation analysis of a larger group of patients from a UK national phase III study to further define the role of epigenetic modifications in disease behaviour and outcome. Patients and Methods Highly purified (>95%) CD138+ myeloma bone marrow cells from 465 newly diagnosed patients enrolled in the UK NCRI Myeloma XI study were analysed. The extracted DNA was bisulfite-converted using the EZ DNA methylation kit (Zymo) and hybridized to Infinium HumanMethylation450 BeadChip arrays. Raw data was processed using the R Bioconductor package "minfi". SNP containing probes and probes on the sex chromosomes were removed. 464 samples and 441293 probes were retained following inspection of quality control metrics. Beta values were summarized across functional genomic units or differentially methylated regions (DMRs) that included: gene bodies, promoters, insulators, CpG-islands and enhancers. K-means was applied to each DMR to cluster patients into 2 groups (high or low methylation) per region. Filters were applied to define a clinically meaningful minimum group size and methylation differences between the groups. Overall survival (OS) and progression free survival (PFS) were assessed by a Cox proportional hazards regression model fitted to each DMR with a time-dependent covariate of the trial pathway. Pathway analyses were performed using GREAT (Stanford University) and GSEA (Broad Institute). Results We identified 589 differentially methylated regions that were significantly associated with PFS and OS when using a cut-off of P<0.01 (log-rank). Of these, 114 DMRs were located within 10kb of a gene transcription start site (TSS). Among these, several genes implicated in myeloma disease biology, such as immune cell-cell interaction genes (e.g. CD226) or stemness-associated transcription factors (e.g. PAX4) were identified to be differentially methylated. Using pathway analysis on all 589 DMRs, Gene Ontology biologic groups were enriched for positive regulation of proliferation, cell migration and cytoskeleton organisation (FDR P<0.05). This was further supported by enrichment of proliferative E2F1 transcription factor target structures (FDR P<0.05). Matched gene expression profiles have been generated and integrated analyses correlating epigenetic with GEP and genetic risk data and individual gene level methylation-expression associations will be presented at the meeting. This data is also being integrated with drug resistance profiles from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al, 2016). Conclusion Epigenetic mechanisms play a significant role in influencing tumour cell behaviour. We have identified here differentially methylated regions that are significantly associated with patient outcome. Pathway analyses suggest an epigenetic regulation of biologic mechanisms involved in high risk disease, such as proliferation and migration. Integration of epigenetic data with matched gene expression profiles is currently ongoing to delineate independent epigenetic biomarkers associated with high risk disease behaviour. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel support; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria. Jackson:MSD: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Kaiser:Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Other: Travel Support; BMS: Consultancy, Other: Travel Support; Chugai: Consultancy.

scholarly journals Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Sophia Clara Mädler ◽  
Alice Julien-Laferriere ◽  
Luis Wyss ◽  
Miroslav Phan ◽  
Anthony Sonrel ◽  
...  
Keyword(s):  
Best Practices ◽  
Translational Research ◽  
Single Cell ◽  
Tumor Tissue ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Supervised Machine Learning ◽  
Rna Seq ◽  
Disease Biology ◽  
Transcriptomics Data

Abstract Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.

scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

scholarly journals Genetic links between endometriosis and cancers in women

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8135 ◽  
Author(s):  
Salma Begum Bhyan ◽  
Li Zhao ◽  
YongKiat Wee ◽  
Yining Liu ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Relationships ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Expression Of Genes ◽  
Genetic Mechanisms

Endometriosis is a chronic disease occurring during the reproductive stage of women. Although there is only limited association between endometriosis and gynecological cancers with regard to clinical features, the molecular basis of the relationship between these diseases is unexplored. We conducted a systematic study by integrating literature-based evidence, gene expression and large-scale cancer genomics data in order to reveal any genetic relationships between endometriosis and cancers in women. We curated 984 endometriosis-related genes from 3270 PubMed articles and then conducted a meta-analysis of the two public gene expression profiles related to endometriosis which identified Differential Expression of Genes (DEGs). Following an overlapping analysis, we identified 39 key endometriosis-related genes common in both literature and DEG analysis. Finally, the functional analysis confirmed that all the 39 genes were associated with the vital processes of tumour formation and cancer progression and that two genes (PGR and ESR1) were common to four cancers of women. From network analysis, we identified a novel linker gene, C3AR1, which had not been implicated previously in endometriosis. The shared genetic mechanisms of endometriosis and cancers in women identified in this study provided possible new avenues of multiple disease management and treatments through early diagnosis.

scholarly journals A simple in silico approach to generate gene-expression profiles from subsets of cancer genomics data

BioTechniques ◽  
2019 ◽  
Vol 67 (4) ◽  
pp. 172-176 ◽  
Author(s):  
Mohammed Khurshed ◽  
Remco J Molenaar ◽  
Cornelis JF van Noorden
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Large Datasets ◽  
Web Interface ◽  
Simple Method ◽  
Clinical Interest

In biomedical research, large-scale profiling of gene expression has become routine and offers a valuable means to evaluate changes in onset and progression of diseases, in particular cancer. An overwhelming amount of cancer genomics data has become publicly available, and the complexity of these data makes it a challenge to perform in silico data exploration, integration and analysis, in particular for scientists lacking a background in computational programming or informatics. Many web interface tools make these large datasets accessible but are limited to process large datasets. To accelerate the translation of genomic data into new insights, we provide a simple method to explore and select data from cancer genomic datasets to generate gene-expression profiles of subsets that are of specific genetic, biological or clinical interest.

scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

Sign in / Sign up

Export Citation Format

Share Document