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  • American Association for Cancer Research (AACR)  (10)
  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. LB-234-LB-234
    Abstract: Central nervous system-related tumors release tumoral material into circulating blood and the cerebrospinal fluid (e.g. cell free DNA). The sampling of these biofluids, i.e. liquid biopsy (LB), may offer an opportunity for diagnosis, prognostication and response prediction in a constantly evolving and biologically and prognostically heterogeneous tumor, such as glioma, in real-time. In glioma-tumor tissue, genome-wide DNA methylation profiling has shown that epigenetic abnormalities play significant biological and clinical roles, making DNA methylome profiling attractive for LB application in these tumors. Thus far, studies of epigenetic LB (eLB) focused on targeted markers which have shown low sensitivity; however, this can be potentially circumvented by a comprehensive genome-wide CpG methylation profiling. Herein, we profiled the genome-wide CpG methylation landscape of matching serum/tissue from 22 patients who received surgical resection for a glioma diagnosis (15 IDH-mutant and 7 IDH-wildtype) and 4 who received surgical resection of the brain for a non-tumor brain related disease. We identified 199 glioma specific DNA methylation-serum based markers (Wilcoxon Rank Sum test, p-value & lt; 0.001) that differentiated glioma from non-tumor brain tissues (diagnostic eLB). These eLB diagnostic markers were found to be enriched for CpG islands and depleted for open seas and shores. Interestingly, CpG methylation of MYC and CD34 promoters, previously described in the tissue, were detectable in the serum of glioma patients as part of the 199 CpG eLB signature. We also identified 987 eLB markers (Wilcoxon Rank Sum test, p-value & lt; 0.01) that discriminated patients with IDH-mutant from IDH-wildtype (prognostic eLB). Among the initial cohort, comprised by 4 MGMT-unmethylated and 18 MGMT-methylated gliomas, we also identified 428 specific eLB markers that discriminated the MGMT status among the patients (predictive eLB). Harnessing DNA methylation data of The Cancer Genome Atlas (TCGA) consortium, derived from 10,000 primary and untreated tumor tissue samples, spanning 33 cancer types, we found our three eLB signatures (diagnostic, prognostic and predictive) to be highly specific to gliomas. Our results suggest that serum eLB profiling may be useful as a surrogate or complementary for tissue-based approach for diagnosis, prognostication and treatment prediction of gliomas. In addition, our eLB signatures can be applied as a real-time non-invasive approach to improve detection of glioma progression and recurrence. Once validated, the application of the eLB panels discovered in this study have the potential to significantly and positively improve the pre- and post-surgical quality of care for patients harboring gliomas. Citation Format: Houtan Noushmehr, Thais S. Sabedot, Tathiane M. Malta, Kevin K. Nelson, James Snyder, Michael Wells, Maritza S. Mosella, Ana C. deCarvalho, Karam Asmaro, Lisa Scarpace, Adam M. Robin, Mark L. Rosenblum, Tom Mikkelsen, Jack Rock, Tobias Walbert, Ian Lee, Laila M. Poisson, Steven N. Kalkanis, Ana V. Castro. Pre-surgical identification of diagnostic, prognostic and predictive DNA methylation-based markers in serum (liquid biopsy) of patients harboring gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-234.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 2168-2168
    Abstract: Diffuse glioma is characterized by a poor prognosis and a universal resistance to therapy, though the evolutionary processes behind this resistance remain unclear. The Glioma Longitudinal Analysis (GLASS) Consortium has previously demonstrated that therapy-induced selective pressures shape the genetic evolution of glioma in a stochastic manner. However, single-cell studies have revealed that malignant glioma cells are highly plastic and transition their cell state in response to diverse challenges, including changes in the microenvironment and the administration of standard-of-care therapy. To interrogate the factors driving therapy resistance in diffuse glioma, we collected and analyzed RNA- and/or DNA-sequencing data from temporally separated tumor pairs of over 300 adult patients with IDH-wild-type or IDH-mutant glioma. In a subset of these tumor pairs, we additionally performed multiplex immunofluorescence to capture the spatial relationship between tumor cells and their microenvironment. Recurrent tumors exhibited diverse changes that were attributable to changes in histological features, somatic alterations, and microenvironment interactions. IDH-wild-type tumors overall were more invasive at recurrence and exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. In contrast, recurrent IDH-mutant tumors exhibited a significant increase in proliferative expression programs that correlated with discrete genetic changes. Hypermutation and acquired CDKN2A homozygous deletions associated with an increase in proliferating stem-like malignant cells at recurrence in both glioma subtypes, reflecting active tumor expansion. A transition to the mesenchymal phenotype was associated with the presence of a specific myeloid cell state defined by unique ligand-receptor interactions with malignant cells, providing opportunities to target this transition through therapy. Collectively, our results uncover recurrence-associated changes in genetics and the microenvironment that can be targeted to shape disease progression following initial diagnosis. Citation Format: Frederick S. Varn, Kevin C. Johnson, Jan Martinek, Jason T. Huse, MacLean P. Nasrallah, Pieter Wesseling, Lee A. Cooper, Tathiane M. Malta, Taylor E. Wade, Thais S. Sabedot, Daniel J. Brat, Peter V. Gould, Adelheid Wöehrer, Kenneth Aldape, Azzam Ismail, Floris P. Barthel, Hoon Kim, Emre Kocakavuk, Nazia Ahmed, Kieron White, Santhosh Sivajothi, Indrani Datta, Jill S. Barnholtz-Sloan, Spyridon Bakas, Fulvio D'Angelo, Hui K. Gan, Luciano Garofano, Mustafa Khasraw, Simona Migliozzi, D. Ryan Ormond, Sun Ha Paek, Erwin G. Van Meir, Annemiek M. Walenkamp, Colin Watts, Michael Weller, Tobias Weiss, Karolina Palucka, Lucy F. Stead, Laila M. Poisson, Houtan Noushmehr, Antonio Iavarone, Roel G. Verhaak, The GLASS Consortium. Longitudinal analysis of diffuse glioma reveals cell state dynamics at recurrence associated with changes in genetics and the microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2168.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. LB-373-LB-373
    Abstract: Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem cell-like features. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation. We took advantage of an innovative one-class logistic regression machine learning algorithm (OCLR) to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progenies. Using OCLR, we were able to sort TCGA tumor samples by stemness phenotype and identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of tumor microenvironment revealed the correlation of cancer stemness with immune checkpoint expression and infiltrating immune system cells not previously anticipated. We have shown the de-differentiated oncogenic phenotype increased in the metastatic tumor that further justify their more aggressive phenotype. Application of our stemness indices reveals features of intra-tumor heterogeneity in molecular profiles obtained from the single-cell analyses. Finally, the machine learning-based indices allowed for the identification of chemical compounds and novel targets for the cancer therapies aiming at tumor differentiation. Our findings provide new prognostic signatures that enable cancer biologists and oncologists to quantify the impact of tumor stemness on outcome across cancer types and may help to pave the way for progress in treatment strategies for cancer patients. Citation Format: Tathiane M. Malta, Artem Sokolov, Andrew J. Gentles, Tomasz Burzykowski, Laila Poisson, John Weinstein, Bożena Kamińska, Joerg Huelsken, Larsson Omberg, Olivier Gevaert, Antonio Colaprico, Patrycja Czerwińska, Sylwia Mazurek, Lopa Mishra, Holger Heyn, Alex Krasnitz, Andrew K. Godwin, Alexander J. Lazar, The Cancer Genome Atlas Research Network, Joshua M. Stuart, Katherine Hoadley, Peter W. Laird, Houtan Noushmehr, Maciej Wiznerowicz. Comprehensive analysis of cancer stemness [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-373.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 8_Supplement ( 2023-04-14), p. LB061-LB061
    Abstract: Progression and therapeutic resistance in cancer have been strongly associated with the acquisition of a stemness phenotype. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation in tumor samples. We used a machine learning model to predict the stemness molecular phenotype based on proteomic data. The prediction model was built from human pluripotent stem cell from the Human Induced Pluripotent Stem Cells Consortium (HipSci) and applied to compute stemness indices on the Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumor samples, consisting in their proteogenomic hallmarks of stemness. The obtained stemness scores based on protein expression are novel and original, and are significantly more robust compared to our previous published work. The obtained proteomic score is able to classify stem cells and non-stem cell classes. The initial analysis of over 2000 tumor samples obtained from twelve types of primary carcinomas of breast, ovary, lung, kidney, uterus, brain (pediatric and adult), head and neck, liver, stomach, colon, and pancreas has confirmed our previously published results. Indexing of CPTAC tumors with proteomic stemness score brought us with previously unappreciated findings. We integrated the stemness scores computed using proteins with gene expression, DNA methylation, microRNA, copy number alteration and protein post-translational modification to identify coherent proteogenomic stemness association. Our initial findings identified proteins and phospho-proteins as active nodes of signaling pathways and transcriptional networks that drive aggressiveness of the primary tumors that cause resistance to existing therapies. The correlation between stemness scores and protein expression resulted in the identification of potential drug targets for anti-cancer therapy both tumor-specific and shared among different tumor types. Our results also revealed stemness-associated proteins predictive of clinical outcome across analyzed tumor types. Finally, we validated some stemness targets by immunohistochemistry in independent samples and confirmed the association with clinical outcome. Targeting the proteins here identified and cellular mechanisms that drive a stemness phenotype with existing or novel drugs may eventually lead for clinical development of effective cures for cancer patients. Citation Format: Tathiane M. Malta, Iga Kołodziejczak, Renan Simões, Antonio Colaprico, Erik Storrs, Francesca Petralia, Felipe da v Leprevost, Rossana L. Segura, Elizabeth Demicco, Alexander J. Lazar, Weiping Ma, Pietro Pugliese, Michele Ceccarelli, Bozena Kamińska, Alexey I. Nesvizhski, Bing Zhang, Henry Rodriguez, Mehdi Mesri, Ana I. Robles, Clinical Proteomic Tumor Analysis Consortium, Li Ding, Maciej Wiznerowicz. Proteomic-based stemness score measure oncogenic dedifferentiation and enable the identification of druggable targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB061.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 5
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    Online Resource
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 781-781
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 781-781
    Abstract: Meningiomas are mostly benign CNS tumors however, a subset of these tumors may become atypical or malignant. The standard of care to monitor patients after diagnosis requires serial MRI assessments, which have limited value in distinguishing malignant tumors from benign disease. Therefore the discovery of non-invasive methodologies that reflect meningioma tumor burden and its dynamic evolution in real-time is highly desirable. Liquid biopsy (LB) could be used to fine-tune surveillance and treatment with minimal risk to patients. Evidence of circulating tumor cells and cell-free (cf) tumor DNA in the blood has been shown in several tumor types however, limited progress has been made for brain tumors with known biomarkers; possibly due to the unlikelihood of capturing point mutations in circulating DNA fragments. On the other hand, DNA methylation signatures are maintained even in small DNA fragments, which suggests that DNA methylation is an attractive biomarker to be studied in liquid biopsy of brain cancers. In order to identify DNA methylation-based biomarkers using archival serum and tissue specimens, we generated and analyzed the epigenome (Illumina Human EPIC array) of patients with meningioma (including primary (n=6); recurrent (n=8)) and non-meningioma (including non-tumor patients (n=5), pituitary tumor (n=13), colorectal cancer (n=2) and other CNS diseases (n=6), such as inflammatory tissue and radiation necrosis). cfDNA fragment size distribution revealed peaks with 150~200bp on average. By randomly selecting 70% of our cohort as a discovery set, we identified 500 CpGs (FDR & lt;0.05) differentially methylated between meningiomas and non-meningiomas, which show a DNA methylation profile similar to the matched meningioma tissue. Then, we trained a random-forest machine learning using the same signature and applied the model to the remaining 30% samples (validation set) from our cohort. The model was able to correctly classify samples into meningioma and non-meningiomas with a sensitivity of 100% and specificity of 100%. From this pilot data, we were able to investigate the LB methylome of meningioma patients and identify potential markers to detect tumor cells in the serum of these patients, which could eventually allow clinicians to monitor impending disease progression and recurrence. Citation Format: Thais S. Sabedot, Tathiane M. Malta, Ruicong She, James Snyder, Tobias Walbert, Ian Lee, Steven Kalkanis, James Ewing, AnaValeria Castro, Houtan Noushmehr. Methylation-based liquid biopsy of meningioma primary and recurrent samples [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 781.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 6
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2020
    In:  Clinical Cancer Research Vol. 26, No. 11_Supplement ( 2020-06-01), p. A12-A12
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 26, No. 11_Supplement ( 2020-06-01), p. A12-A12
    Abstract: Meningiomas are mostly benign CNS tumors; however, a subset of these tumors may become atypical or malignant. The standard of care to monitor patients after diagnosis requires serial MRI assessments, which have limited value in distinguishing malignant tumors from benign disease. Therefore, the discovery of noninvasive methodologies that reflect meningioma tumor burden and its dynamic evolution in real-time is highly desirable. Liquid biopsy (LB) could be used to fine-tune surveillance and treatment with minimal risk to patients. Evidence of circulating tumor cells and cell-free (cf) tumor DNA in the blood has been shown in several tumor types; however, limited progress has been made for brain tumors with known biomarkers, possibly due to the unlikelihood of capturing point mutations in circulating DNA fragments. On the other hand, DNA methylation signatures are maintained even in small DNA fragments, which suggests that DNA methylation is an attractive marker to be studied in liquid biopsy of brain cancers. In order to identify DNA methylation-based biomarkers, we used archival serum and matching tissue specimens from primary (n=10) and recurrent (n=4) meningiomas. From isolated cfDNA from meningiomas and epileptic patients (n=5) as a control, we generated epigenetic data using Illumina Human EPIC array. cfDNA fragment size distribution revealed peaks with 150~200bp on average. We identified 482 CpGs (FDR & lt;0.001) differentially methylated between primary meningiomas and controls, of which 294 (61%) show a DNA methylation profile similar to the epigenome of the matched tumor tissue. Overall, we observed that recurrent samples are hypermethylated (56%) compared to primary. From this pilot data, we were able to investigate the LB methylome of meningioma patients and identify potential markers to detect tumor cells in the serum of these patients, which could eventually allow clinicians to monitor impending disease progression and recurrence. Citation Format: Thais S. Sabedot, Tathiane M. Malta, James Snyder, Tobias Walbert, Ian Lee, Steven Kalkanis, Ana Valeria Castro, Houtan Noushmehr. DNA methylation-based liquid biopsy detects primary and recurrent meningioma [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A12.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 7
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 841-841
    Abstract: Although most meningioma are non-malignant, there is a high recurrence rate among atypical and anaplastic (malignant) meningiomas (grades II/III). In addition, malignant meningioma usually progresses after treatment. Recently, based on DNA methylation, two subgroups of meningioma were described with recurrence-free survival differences. Epigenetic deregulation at distinct genomic elements can affect changes in gene expression and alter the transcriptional profile of the cancer cells. We seek to understand the mechanisms of meningioma recurrence and progression after initial treatment. In order to address this, we will use DNA methylation data to identify candidate noncoding elements and their connection with genes that might explain differences in meningioma prognostic subgroups. Using published DNA methylation data we compared favorable and unfavorable meningioma subgroups and identified 3,045 differentially methylated probes (p & lt; 0.0001, difference mean-methylation beta-value & gt; 0.2). Focusing on probes within known functional genomics, we identified 18 highly conserved genomic enhancers known to be activated in cancer that can potentially drive meningioma recurrence. We next investigated links between these enhancers and their targeted genes by incorporating GeneHancer annotation. We found that the unfavorable subgroup of meningiomas presented hypomethylation within enhancer regions that have the potential to target PARK7, ARID4B, and FBH1. ARID4B was previously shown to be highly active in high-grade meningiomas. We also identified 16 enhancer regions that overlap known prognostic cancer enhancer, previously identified in other tumor types. Our findings were validated in independent cohort comprised by public and unpublished DNA methylation datasets. Our preliminary results are the first to suggest that DNA methylation changes can be used to identify noncoding regions associated with meningioma prognosis. Identification of noncoding regions associated with meningioma recurrence will provide knowledge of the role of epigenomics in the development of malignant meningioma and of opportunities for targeted therapy. Citation Format: Tathiane M. Malta, James Snyder, Michael Wells, Ana deCarvalho, Laila Poisson, Camila Souza, Gelareh Zadeh, Kenneth Aldape, Daniela Tirapelli, Carlos Carlotti, Yan Lee, Steven Kalkanis, Tobias Walbert, Houtan Noushmehr. Meningioma subgroups associated with functional genomic elements defined by DNA methylation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 841.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 8
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 9 ( 2020-05-01), p. 1819-1832
    Abstract: RING-finger E3 ligases are instrumental in the regulation of inflammatory cascades, apoptosis, and cancer. However, their roles are relatively unknown in TGFβ/SMAD signaling. SMAD3 and its adaptors, such as β2SP, are important mediators of TGFβ signaling and regulate gene expression to suppress stem cell–like phenotypes in diverse cancers, including hepatocellular carcinoma (HCC). Here, PJA1, an E3 ligase, promoted ubiquitination and degradation of phosphorylated SMAD3 and impaired a SMAD3/β2SP-dependent tumor-suppressing pathway in multiple HCC cell lines. In mice deficient for SMAD3 (Smad3+/−), PJA1 overexpression promoted the transformation of liver stem cells. Analysis of genes regulated by PJA1 knockdown and TGFβ1 signaling revealed 1,584 co-upregulated genes and 1,280 co-downregulated genes, including many implicated in cancer. The E3 ligase inhibitor RTA405 enhanced SMAD3-regulated gene expression and reduced growth of HCC cells in culture and xenografts of HCC tumors, suggesting that inhibition of PJA1 may be beneficial in treating HCC or preventing HCC development in at-risk patients. Significance: These findings provide a novel mechanism regulating the tumor suppressor function of TGFβ in liver carcinogenesis.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 9
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    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 6563-6563
    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
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 10
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    Online Resource
    American Association for Cancer Research (AACR) ; 2021
    In:  Cancer Research Vol. 81, No. 13_Supplement ( 2021-07-01), p. 2717-2717
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 13_Supplement ( 2021-07-01), p. 2717-2717
    Abstract: Background: Gliomas are the most common malignant brain tumor, have a very aggressive behavior, and invariably relapse and progress. Despite the recent advances, only a few drugs are approved and they present limited success. Currently, there are numerous clinical trials evaluating the efficacy of immunotherapy for gliomas, which are not completed yet. Deciphering the composition of the tumor microenvironment (TME) can have an important and immediate impact on therapeutic interventions and on the development of prognostic and predictive biomarkers for gliomas immunotherapy. To investigate the molecular dynamics over time and in response to therapeutic pressures, the Glioma Longitudinal AnalySiS (GLASS) Consortium, a multinational collaboration, is investigating epigenome-wide molecular data from primary and recurrent matched pairs. Objective: Our aim is to evaluate glioma TME using the deconvolution method methylCIBERSORT applied to DNA methylation data from GLASS. Methods: We generated and validated a customized reference signature defining 10 cell types to predict the relative proportions of immune cell type in the TME of 370 glioma specimens, including 132 longitudinal pairs (initial and recurrent tumors) in association with clinical features (recurrence, survival etc). Results: We found that the TME differs across gliomas of different subtypes. In general, IDHmut subtypes (Codel, GCIMP-high, and GCIMP-low) presented less immune infiltration than IDHwt (Classic-like, Mesenchymal-like, and PA-like). The most abundant estimated infiltrated cell types in IDHmut and IDHwt gliomas were TCD4 cells and macrophages, respectively. Post-treatment (chemo+radiotherapy), we found a decrease of TCD4 and an increase of TCD8 cells in recurrent Codel and G-CIMP-high subtypes; and an increase of macrophages in classic recurrent tumors. High frequency of macrophages and TCD8 cells were associated with poorer overall survival in the IDHwt patients (log-rank p=0.040, hazard ratio (HR) = 1.38; log-rank p=0.046, HR = 2.37, respectively). Conclusions: Using a DNA methylation-based deconvolution approach, we have described the TME of longitudinal gliomas. We found a TME diversity across glioma molecular subtypes and an association with IDH mutation and overall survival. Our findings indicate that the epigenomic deconvolution of TME has a potential therapeutic and prognostic implication to guide the management of patients with gliomas. Citation Format: Tathiane Maistro Malta, Indrani Datta, Thais Sabedot, Ruicong She, AnaValeria Castro, GLASS Consortium GLASS Consortium, Antonio Iavarone, Laila M. Poisson, Houtan Noushmehr. Glioma immune microenvironment change during tumor recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2717.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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