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  • 1
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    Online Resource
    EPCO-46. EPIGENETIC STEMNESS PREDICTION ACROSS CENTRAL NERVOUS SYSTEM TUMORS MOLECULAR SUBTYPES
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii126-vii126
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii126-vii126
    Abstract: Tumor progression and therapeutic resistance in cancer have been strongly associated with stemness. Rather than focus on a discrete subpopulation for stemness and tumor maintenance, we might explore stemness as a continuous variable through machine learning algorithms. We chose One Class Logistic Regression algorithm to define an epigenetic signature to assess stemness in brain tumors using induced Neural Stem Cell (iNSC) through public DNA methylation data. In order to keep DNA methylation features most related to iNSC, we perform Wilcoxon test between 9 iNSC and 128 non-tumor brain tissue (methylation difference |0.3|, FDR & lt; 0.01) and mapped the resultant features to genome regions. Our model revealed a positive correlation (r2 = 0.86 p & lt; 0.001) with the pluripotent model from Malta et al. (2018) applied on TCGA gliomas. More remarkable, the iNSC model stratified IDHwt glioma survival by the median of stemness both on TCGA and GLASS cohorts (TCGA: p & lt; 0.001, GLASS: p = 0.086; Likelihood ratio test). Having shown its potential, we next applied the iNSC model in other Central Nervous System (CNS) tumors using cohorts studied by Capper et al. (2016). The iNSC model stratified distinct DNA methylation-based subtypes, such as: i) ependymoma-RELA presented a very distinct and lowest stemness among all ependymal subtypes; ii) low stemness in lymphoma and high stemness in plasmacytoma; iii) low stemness pituitary-ACTH and high stemness pituitary-STH-DNS-B. Interestingly, gliomas IDHmutant have the lowest stemness. Additionally, among putative new entities identified by Capper, our iNSC model stratified high-grade-neuroepithelial-BCOR with the lowest stemness among embryonal tumors and infantile-hemispheric-glioma, high-grade-neuroepithelial-MN1, and anaplastic-pilocytic-astrocytoma showing differences among each other in “Other glioma” group. Our results indicate prognosis prediction for gliomas and recapitulate CNS tumors subgroups, which might suggest the iNSC model as a surrogate strategy to explore the stemness in brain tumors from an epigenetic perspective.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.480
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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  • 2
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    Abstract 6563: Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 6563-6563
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 6563-6563
    Abstract: Adult diffuse gliomas are heterogeneous and the most common primary brain tumors. Gliomas have high tissue invasion, proliferation and therapeutic resistance potential. Although much knowledge has been recently gained regarding glioma biology and evolution, many questions are still open, such as which is the cell of origin, what are its characteristics and how the tumor propagation occurs. The cancer stem cell (CSC) model proposes a population of cells with high self-renew capacity and capable of propagating the tumor with more differentiated cells, generating intratumoral heterogeneity. Based on the CSC model, our work aims to integrate the most advanced techniques available such as scRNAseq and machine learning models to estimate the enrichment of glioma stem cells (GSCs) in tumors, by defining a GSC-Stemness Index (GSCsi). To build the prediction model, we used public scRNA-seq data from glioblastoma (GBM)-enriched GSCs. We used the standard Seurat pipeline for quality control, normalization and downstream analysis. The GSC single cell data was used to train a prediction model using the One Class Logistic Regression (OCLR) algorithm. Several models were tested, including overdispersed genes, differentially expressed genes between GSCs and the whole tumor, GSCs from each patient individually and a model with all GSCs. The prediction models were applied to gene expression data from TCGA, GLASS, and publicly available scRNA-seq data from different glioma subtypes. The model built with all the GSCs and all genes showed the best performance, being able to identify with higher indices grade 4 gliomas and IDHwt in the TCGA and GLASS data. Survival analysis resulted in a hazard ratio greater than 20, indicating a high correlation between increased GSCsi and poor prognosis. By applying the model to scRNA-seq data from gliomas, clusters of high GSCsi were identified. We can partially conclude that the GSCsi obtained with the model is capable of identifying grade 4 gliomas and IDHwt and we are performing analyzes with the genes with the highest correlation (positive and negative) with the GSCsi in the TCGA and GLASS data. The analysis of these genes together with genes differentially expressed in the high GSCsi clusters in scRNA-seq data can elucidate pathways responsible for the therapeutic resistance and propagation of gliomas, in addition to proposing theories about the cell of origin and potential therapeutic targets to improve the diagnosis and treatment of these patients. Citation Format: Renan L. Simões, Maycon Marção, Tathiane M. Malta. Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6563.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-6563
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 3
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    Table4.xlsx
    Frontiers Media SA ; 2022
    In:  Frontiers in Drug Discovery Vol. 2 ( 2022-8-10)
    Details
    In: Frontiers in Drug Discovery, Frontiers Media SA, Vol. 2 ( 2022-8-10)
    Abstract: Stemness is a phenotype associated with cancer initiation and progression, malignancy, and therapeutic resistance, exhibiting particular molecular signatures. Targeting stemness has been proposed as a promising strategy against breast cancer stem cells that can play a key role in breast cancer progression, metastasis, and multiple drug resistance. Here, using a previously published one-class logistic regression machine learning algorithm (OCLR) built on pluripotent stem cells to predict stemness in human cancer samples, we provide the stemness index (mRNAsi) of different canine non-tumor and mammary cancer cells. Then, we confirmed that inhibition of BET proteins by (+)-JQ1 reduces stemness in a high mRNAsi canine cancer cell. Furthermore, using public data, we observed that (+)-JQ1 can also decrease stemness in human triple-negative breast cancer cells. Our work suggests that mRNAsi can be used to estimate stemness in different species and confirm epigenetic modulation by BET inhibition as a promising strategy for modulating the stemness phenotype in canine and human mammary cancer cells.
    Type of Medium: Online Resource
    ISSN: 2674-0338
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fddsv.2022.953988
    DOI: 10.3389/fddsv.2022.953988.s001
    DOI: 10.3389/fddsv.2022.953988.s002
    DOI: 10.3389/fddsv.2022.953988.s003
    DOI: 10.3389/fddsv.2022.953988.s004
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 3123815-4
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  • 4
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    Online Resource
    EPCO-48. STEMNESS SIGNATURE STRATIFIES GLIOMAS WITH CLINICAL IMPORTANCE AND REVEALS A PROLIFERATIVE PHENOTYPE IN SINGLE CELL DATA
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii127-vii127
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii127-vii127
    Abstract: Gliomas are the most common central nervous system neoplasm and despite the past significant progress, its diagnostics faces suboptimal classification which impacts patient management. The stem cell-like phenotype of various cancers is correlated with the worst overall prognosis. We propose a Stemness prediction model based on gene expression signatures of neural progenitors that can be used to measure the dedifferentiation state (or Stemness) of glioma samples. To built the model, publicly available single-cell RNA sequencing data was used to identify gene expression from the fetal astrocyte (AST) population. Subpopulations of interest were identified through the expression of marker genes. We applied a one-class logistic regression to built the prediction model using the AST population. The model was applied to glioma bulk transcriptomic data to generate an fetal astrocyte stemness index (ASTsi). The ASTsi was able to stratify gliomas based on grade, histology, and molecular subtypes. Grade 4, glioblastoma, IDHwt, and the mitochondrial and proliferative functional subtypes had the highest stemness. When applied to longitudinal samples we observed an increase of ASTsi in IDHmut recurrent and a decrease in IDHwt recurrent tumors, compared to primary samples. Additionally, we applied the model to single-cell RNAseq of adult IDHwt glioblastomas and found clusters of high-stemness cells (ASTsi & gt; 0.8). A differential gene expression combined with pathway analysis between high- and low-stemness cells revealed cell cycle, DNA repair mechanisms and histone modifications upregulated in the high-stemness population. The balance between histone methylation and demethylation may be directly related to the phenotype of these cells. More in-depth analysis of these genes and pathways are being carried out and may provide important information about the oncogenesis and phenotypic characterization of glioma stemness. Our stemness prediction model stratified glioma samples by pathological and molecular features and revealed tumor subpopulations with distinct stemness degree in gliomas IDHwt.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.482
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2094060-9
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  • 5
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    Online Resource
    Abstract 1968: A multiomic spatial atlas of breast cancer in women of African ancestry
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 1968-1968
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 1968-1968
    Abstract: The US Black population consists of both US-born Black and immigrant Black populations from the Caribbean and Africa. Normal tissues in Black individuals, independent of their country of birth or residence, are woefully understudied. Yet, Black individuals disproportionately develop aggressive pathologic diseases and are treatment refractory or resistant, thus leaing to premature deaths. In women, breast cancer is more common among US Black women, and the most common non-viral driven cancer in African and Caribbean countries. Furthermore, Black women develop this disease younger than other ancestral groups and have a higher incidence aggressive pathologies, such as metaplastic and triple-negative breast cancer. With the African-Caribbean Cancer Consortium (AC3) and Transatlantic Gynecologic Cancer Research Consortium, we have created a multiomic spatial atlas of triple-negative and other breast cancers across Africa, the Caribbean, and among US-Black individuals. We performed ultrahigh-plex RNA and protein spatial phenotyping on the PhenoCycler-Fusion (PCF). The PCF is a fast end-to-end spatial biology platform that enables whole-slide spatial readouts of RNA and protein moieties at single-cell resolution. Multiomic spatial phenotyping of tissues allowed for the detection of novel cell populations associated with a given African ancestry linking unique immune/stromal cell types to the outcome and severity of breast cancer. Here, we aim to develop a benchmark that confidently measures and interprets ancestral genomic differences at the cellular level. Deciphering the relationship between African ancestry, aggressive disease biology, and early onset will enable the characterization of the tissue composition and the proportion of cell sub-populations implicated in tumorigenesis and the interplay with germline genetics. Citation Format: Jasmine T. Plummer, Nadezhda Nikulina, Ayodele Omotoso, Destiny Burnett, Priscilla Coelho, Simone Badal, Judith Hurley, Carmen Gomez, Ha Yeun Ji, Maycon Marcao, Felipe Dezem Segato, Oliver Braubach, Sophia George. A multiomic spatial atlas of breast cancer in women of African ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1968.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-1968
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 6
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    Online Resource
    Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas
    Elsevier BV ; 2024
    In:  Heliyon Vol. 10, No. 5 ( 2024-03), p. e26714-
    Details
    In: Heliyon, Elsevier BV, Vol. 10, No. 5 ( 2024-03), p. e26714-
    Type of Medium: Online Resource
    ISSN: 2405-8440
    URL: Article
    DOI: 10.1016/j.heliyon.2024.e26714
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2835763-2
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