scholarly journals Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs

NAR Cancer ◽  
2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Adrienne Vancura ◽  
Andrés Lanzós ◽  
Núria Bosch-Guiteras ◽  
Mònica Torres Esteban ◽  
Alejandro H Gutierrez ◽  
...  
Keyword(s):  
High Throughput ◽  
Insertional Mutagenesis ◽  
Somatic Mutations ◽  
Annotation Scheme ◽  
Genomic Features ◽  
Gene Sets ◽  
Therapeutic Development ◽  
Non Coding Rnas ◽  
Clinical Genomic ◽  
Suppressor Analysis

Abstract Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance.

scholarly journals Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs

2020 ◽  
Author(s):  
Adrienne Vancura ◽  
Andrés Lanzós ◽  
Núria Bosch ◽  
Mònica Torres ◽  
Alejandro Hionides Gutierrez ◽  
...  
Keyword(s):  
High Throughput ◽  
Insertional Mutagenesis ◽  
Somatic Mutations ◽  
High Confidence ◽  
Annotation Scheme ◽  
Genomic Features ◽  
Gene Sets ◽  
Therapeutic Development ◽  
Clinical Genomic ◽  
Suppressor Analysis

AbstractLong noncoding RNAs play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here we present the Cancer LncRNA Census 2 (CLC2): at 492 cancer lncRNAs, it is 4-fold greater than its predecessor, without compromising on strict criteria of confident functional / genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis (TIM) screening data, yielding 95 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, holds the greatest number of unique genes, and carries functional labels (oncogene / tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations, and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical / genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance.

scholarly journals High-throughput insertional mutagenesis reveals novel targets for enhancing lipid accumulation in Nannochloropsis oceanica

2021 ◽  
Author(s):  
Christian Südfeld ◽  
Michal Hubáček ◽  
Daniel Rodrigues Figueiredo ◽  
Mihris I.S. Naduthodi ◽  
John van der Oost ◽  
...  
Keyword(s):  
High Throughput ◽  
Lipid Accumulation ◽  
Insertional Mutagenesis ◽  
Nannochloropsis Oceanica ◽  
Novel Targets

scholarly journals Protocol to analyze circulating small non-coding RNAs by high-throughput RNA sequencing from human plasma samples

2021 ◽  
Vol 2 (3) ◽  
pp. 100606
Author(s):  
Giuseppina E. Grieco ◽  
Guido Sebastiani ◽  
Daniela Fignani ◽  
Noemi Brusco ◽  
Laura Nigi ◽  
...  
Keyword(s):  
Human Plasma ◽  
Rna Sequencing ◽  
High Throughput ◽  
Plasma Samples ◽  
Human Plasma Samples ◽  
Non Coding Rnas

scholarly journals Distinct genomic features across cytolytic subgroups in skin melanoma

2021 ◽  
Author(s):  
Constantinos Roufas ◽  
Ilias Georgakopoulos-Soares ◽  
Apostolos Zaravinos
Keyword(s):  
Immune Checkpoint ◽  
Cytolytic Activity ◽  
Checkpoint Inhibition ◽  
Immune Checkpoint Inhibition ◽  
Skin Melanoma ◽  
Related Gene ◽  
Mutational Signatures ◽  
Genomic Features ◽  
Immune Related Gene ◽  
Gene Sets

Abstract Background Skin melanoma is a highly immunogenic cancer. The intratumoral immune cytolytic activity (CYT) reflects the ability of cytotoxic T and NK cells to eliminate cancer cells, and is associated with improved patient survival. Despite the enthusiastic clinical results seen in advanced-stage metastatic melanoma patients treated with immune checkpoint inhibitors, a subgroup of them will later relapse and develop acquired resistance. We questioned whether CYT associates with different genomic profiles and thus, patient outcome, in skin melanoma. Methods We explored the TCGA-SKCM dataset and stratified patients to distinct subgroups of cytolytic activity. The tumor immune contexture, somatic mutations and recurrent copy number aberrations were calculated using quanTIseq, MutSigCV and GISTIC2. Chromothriptic events were explored using CTLPScanner and cancer neoepitopes were predicted with antigen garnish. Each tumor's immunophenoscore was calculated using Immunophenogram. Mutational signatures and kataegis were explored using SigProfiler and compared to the known single or doublet base substitution signatures from COSMIC. Results Metastatic skin melanomas had significantly higher CYT levels compared to primary tumors. We assessed enrichment for immune-related gene sets within CYT-high tumors, whereas, CYT-low tumors were enriched for non-immune related gene sets. In addition, distinct mutational and neoantigen loads, primarily composed of C > T transitions, along with specific types of copy number aberrations, characterized each cytolytic subgroup. We found a broader pattern of chromothripsis across CYT-low tumors, where chromosomal regions harboring chromothriptic events, contained a higher number of cancer genes. SBS7a/b, SBS5 and SBS1 were the most prevalent mutational signatures across both cytolytic subgroups, but SBS1 differed significantly between them. SBS7a/b was mutually exclusive with SBS5 and SBS1 in both CYT subgroups. CYT-high patients had markedly higher immunophenoscore, suggesting that they should display a clinical benefit upon treatment with immune checkpoint inhibition therapy, compared to CYT-low patients. Conclusions Overall, our data highlight the existence of distinct genomic features across cytolytic subgroups in skin melanoma, which might affect the patients' relapse rate or their acquisition of resistance to immune checkpoint inhibition therapies.

scholarly journals kataegis: an R package for identification and visualization of the genomic localized hypermutation regions using high-throughput sequencing

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xue Lin ◽  
Yingying Hua ◽  
Shuanglin Gu ◽  
Li Lv ◽  
Xingyu Li ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Somatic Mutations ◽  
R Package ◽  
Frequency Of Occurrence ◽  
Link Type ◽  
Genomic Landscape ◽  
One Step ◽  
Flanking Regions

Abstract Background Genomic localized hypermutation regions were found in cancers, which were reported to be related to the prognosis of cancers. This genomic localized hypermutation is quite different from the usual somatic mutations in the frequency of occurrence and genomic density. It is like a mutations “violent storm”, which is just what the Greek word “kataegis” means. Results There are needs for a light-weighted and simple-to-use toolkit to identify and visualize the localized hypermutation regions in genome. Thus we developed the R package “kataegis” to meet these needs. The package used only three steps to identify the genomic hypermutation regions, i.e., i) read in the variation files in standard formats; ii) calculate the inter-mutational distances; iii) identify the hypermutation regions with appropriate parameters, and finally one step to visualize the nucleotide contents and spectra of both the foci and flanking regions, and the genomic landscape of these regions. Conclusions The kataegis package is available on Bionconductor/Github (https://github.com/flosalbizziae/kataegis), which provides a light-weighted and simple-to-use toolkit for quickly identifying and visualizing the genomic hypermuation regions.

scholarly journals The Application of Machine Learning to Improve the Subclassification and Prognostication of Acute Myeloid Leukemia

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 28-28
Author(s):  
Hassan Awada ◽  
Arda Durmaz ◽  
Carmel Gurnari ◽  
Ashwin Kishtagari ◽  
Manja Meggendorfer ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Somatic Mutations ◽  
Cross Validation ◽  
Steering Committee ◽  
Advisory Board ◽  
Advisory Committees ◽  
Genomic Features ◽  
Acute Myeloid

Genetic mutations (somatic or germline), cytogenetic abnormalities and their combinations contribute to the heterogeneity of acute myeloid leukemia (AML) phenotypes. To date, prototypic founder lesions [e.g., t(8;21), inv(16), t(15;17)] define only a fraction of AML subgroups with specific prognoses. Indeed, in a larger proportion of AML patients, somatic mutations or cytogenetic abnormalities potentially serve as driver lesions in combination with numerous acquired secondary hits. However, their combinatorial complexity can preclude the resolution of distinct genomic classifications and overlap across classical pathomorphologic AML subtypes, including de novo/primary (pAML) and secondary AML (sAML) evolving from an antecedent myeloid neoplasm (MN). These prognostically discrete AML subtypes are themselves nonspecific due to variable understanding of their pathogenetic links, especially in cases without overt dysplasia. Without dysplasia, reliance is mainly on anamnestic clinical information that might be unavailable or cannot be correctly assigned due to a short prodromal history of antecedent MN. We explored the potential of genomic markers to sub-classify AML objectively and provide unbiased personalized prognostication, irrespective of the clinicopathological information, and thus become a standard in AML assessment. We collected and analyzed genomic data from a multicenter cohort of 6788 AML patients using standard and machine learning (ML) methods. A total of 13,879 somatic mutations were identified and used to predict traditional pathomorphologic AML classifications. Logistic regression modeling (LRM) detected mutations in CEBPA (both monoallelic "CEBPAMo" and biallelic "CEBPABi"), DNMT3A, FLT3ITD, FLT3TKD, GATA2, IDH1, IDH2R140, NRAS, NPM1 and WT1 being enriched in pAML while mutations in ASXL1, RUNX1, SF3B1, SRSF2, U2AF1, -5/del(5q), -7/del(7q), -17/del(17P), del(20q), +8 and complex karyotype being prevalent in sAML. Despite these significant findings, the genomic profiles of pAML vs. sAML identified by LRM resulted in only 74% cross-validation accuracy of the predictive performance when used to re-assign them. Therefore, we applied Bayesian Latent Class Analysis that identified 4 unique genomic clusters of distinct prognoses [low risk (LR), intermediate-low risk (Int-Lo), intermediate-high risk (Int-Hi) and high risk (HR) of poor survival) that were validated by survival analysis. To link each prognostic group to pathogenetic features, we generated a random forest (RF) model that extracted invariant genomic features driving each group and resulted in 97% cross-validation accuracy when used for prognostication. The model's globally most important genomic features, quantified by mean decrease in accuracy, included NPM1MT, RUNX1MT, ASXL1MT, SRSF2MT, TP53MT, -5/del(5q), DNMT3AMT, -17/del(17p), BCOR/L1MT and others. The LR group was characterized by the highest prevalence of normal cytogenetics (88%) and NPM1MT (100%; 86% with VAF>20%) with co-occurring DNMT3AMT (52%), FLT3ITD-MT (27%; 91% with VAF <50%), IDH2R140-MT (16%, while absent IDH2R172-MT), and depletion or absence of ASXL1MT, EZH2MT, RUNX1MT, TP53MT and complex cytogenetics. Int-Lo had a higher percentage of abnormal cytogenetics cases than LR, the highest frequency of CEBPABi-MT (9%), IDH2R172K-MT (4%), FLT3ITD-MT (14%) and FLT3TKD-MT (6%) occurring without NPM1MT, while absence of NPM1MT, ASXL1MT, RUNX1MT and TP53MT. Int-Hi had the highest frequency of ASXL1MT (39%), BCOR/L1MT (16%), DNMT3AMT without NPM1MT (19%), EZH2MT (9%), RUNX1MT (52%), SF3B1MT (7%), SRSF2MT (38%) and U2AF1MT (12%). Finally, HR had the highest prevalence of abnormal cytogenetics (96%), -5/del(5q) (68%), -7del(7q) (35%), -17del(17p) (31%) and the highest odds of complex karyotype (76%) as well as TP53MT (70%). The model was then internally and externally validated using a cohort of 203 AML cases from the MD Anderson Cancer Center. The RF prognostication model and group-specific survival estimates will be available via a web-based open-access resource. In conclusion, the heterogeneity inherent in the genomic changes across nearly 7000 AML patients is too vast for traditional prediction methods. Using newer ML methods, however, we were able to decipher a set of prognostic subgroups predictive of survival, allowing us to move AML into the era of personalized medicine. Disclosures Advani: OBI: Research Funding; Abbvie: Research Funding; Macrogenics: Research Funding; Glycomimetics: Consultancy, Other: Steering committee/ honoraria, Research Funding; Immunogen: Research Funding; Seattle Genetics: Other: Advisory board/ honoraria, Research Funding; Amgen: Consultancy, Other: steering committee/ honoraria, Research Funding; Kite: Other: Advisory board/ honoraria; Pfizer: Honoraria, Research Funding; Novartis: Consultancy, Other: advisory board; Takeda: Research Funding. Ravandi:Abbvie: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria, Research Funding; Orsenix: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Xencor: Consultancy, Honoraria, Research Funding; Macrogenics: Research Funding; BMS: Consultancy, Honoraria, Research Funding. Carraway:Novartis: Consultancy, Speakers Bureau; Takeda: Other: Independent Advisory Committe (IRC); Stemline: Consultancy, Speakers Bureau; BMS: Consultancy, Other: Research support, Speakers Bureau; Abbvie: Other: Independent Advisory Committe (IRC); ASTEX: Other: Independent Advisory Committe (IRC); Jazz: Consultancy, Speakers Bureau. Saunthararajah:EpiDestiny: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Kantarjian:Sanofi: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Honoraria, Research Funding; BMS: Research Funding; Abbvie: Honoraria, Research Funding; Aptitute Health: Honoraria; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Jazz: Research Funding; Immunogen: Research Funding; Adaptive biotechnologies: Honoraria; Ascentage: Research Funding; Amgen: Honoraria, Research Funding; BioAscend: Honoraria; Delta Fly: Honoraria; Janssen: Honoraria; Oxford Biomedical: Honoraria. Kadia:Pfizer: Honoraria, Research Funding; Novartis: Honoraria; Cyclacel: Research Funding; Ascentage: Research Funding; Astellas: Research Funding; Cellenkos: Research Funding; JAZZ: Honoraria, Research Funding; Astra Zeneca: Research Funding; Celgene: Research Funding; Incyte: Research Funding; Pulmotec: Research Funding; Abbvie: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Amgen: Research Funding. Sekeres:Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Consultancy, Membership on an entity's Board of Directors or advisory committees. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria.

scholarly journals High-Throughput Quantification of In Vivo Adeno-Associated Virus Transduction with Barcoded Non-Coding RNAs

2019 ◽  
Vol 30 (8) ◽  
pp. 946-956 ◽  
Author(s):  
Meiyu Xu ◽  
Jia Li ◽  
Jun Xie ◽  
Ran He ◽  
Qin Su ◽  
...  
Keyword(s):  
High Throughput ◽  
Adeno Associated Virus ◽  
Non Coding Rnas

scholarly journals Epigenomic, genomic, and transcriptomic landscape of schwannomatosis

2020 ◽  
Vol 141 (1) ◽  
pp. 101-116
Author(s):  
Sheila Mansouri ◽  
Suganth Suppiah ◽  
Yasin Mamatjan ◽  
Irene Paganini ◽  
Jeffrey C. Liu ◽  
...  
Keyword(s):  
Gene Fusion ◽  
Somatic Mutations ◽  
Genomic Analysis ◽  
Germline Mutations ◽  
Tumor Location ◽  
Molecular Signature ◽  
Structural Rearrangements ◽  
Genomic Features ◽  
Cancer Predisposition Syndrome ◽  
Neuronal Tumors

AbstractSchwannomatosis (SWNTS) is a genetic cancer predisposition syndrome that manifests as multiple and often painful neuronal tumors called schwannomas (SWNs). While germline mutations in SMARCB1 or LZTR1, plus somatic mutations in NF2 and loss of heterozygosity in chromosome 22q have been identified in a subset of patients, little is known about the epigenomic and genomic alterations that drive SWNTS-related SWNs (SWNTS-SWNs) in a majority of the cases. We performed multiplatform genomic analysis and established the molecular signature of SWNTS-SWNs. We show that SWNTS-SWNs harbor distinct genomic features relative to the histologically identical non-syndromic sporadic SWNs (NS-SWNS). We demonstrate the existence of four distinct DNA methylation subgroups of SWNTS-SWNs that are associated with specific transcriptional programs and tumor location. We show several novel recurrent non-22q deletions and structural rearrangements. We detected the SH3PXD2A-HTRA1 gene fusion in SWNTS-SWNs, with predominance in LZTR1-mutant tumors. In addition, we identified specific genetic, epigenetic, and actionable transcriptional programs associated with painful SWNTS-SWNs including PIGF, VEGF, MEK, and MTOR pathways, which may be harnessed for management of this syndrome.

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