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Defining stem cell epigenomic signatures in 33 different tumor types across 9000+ tumor samples

Grant number: 16/01975-3
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): September 01, 2016
Effective date (End): August 31, 2017
Field of knowledge:Health Sciences - Medicine - Medical Clinics
Principal researcher:Houtan Noushmehr
Grantee:Tathiane Maistro Malta Pereira
Supervisor abroad: Ana de Carvalho
Home Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Research place: Henry Ford Health System, United States  
Associated to the scholarship:14/02245-3 - Identification of epigenomic signatures that define open chromatin regulatory networks associated with mesenchymal differentiation from human pluripotent stem cells, BP.PD

Abstract

Tumor cells mirrored the high proliferative capacity and phenotypic plasticity of stem cells. The stem cell-like phenotype of various cancer subtypes is directly correlated with increased aggressiveness, resistance to therapies, and worst overall prognosis. However, it is unclear if different subtypes of late-stage cancer vary in stemness properties and whether or not these subtypes are molecularly similar to normal stem cells. Recent molecular characterization studies have benefited from the availability of the datasets generated by The Cancer Genome Atlas (TCGA) enabling more precise molecular-based classification of tumors and relating molecular signatures with prognosis. Notably, DNA methylation has been shown to play important contribution to tumor biology and classification. In this scientific proposal, by using DNA methylation data from 10,000 samples available at TCGA, across 33 different types of tumors, we aim to identify tumors with stem cells signatures and correlate with clinical features in order to expand our comprehension of tumor biology. The identification of tumors with stem cell phenotype may improve the current tumor classification and in turn may allow better prediction of tumor behavior and ultimately may lead to preventive therapies for individuals at high risk of developing cancer. By integrating stem cell signatures and cancer, we expect our findings will enrich our understanding of tumor biology and open opportunities for improved therapeutic protocols. In addition, the resulting collaboration in this initiative have complementary expertise that will ensure a robust exchange of expertise and the accomplishment of this current proposal. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MALTA, TATHIANE M.; DE SOUZA, CAMILA F.; SABEDOT, THAIS S.; SILVA, TIAGO C.; MOSELLA, MARITZA S.; KALKANIS, STEVEN N.; SNYDER, JAMES; CASTRO, ANA VALERIA B.; NOUSHMEHR, HOUTAN. Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications. NEURO-ONCOLOGY, v. 20, n. 5, p. 608-620, MAY 2018. Web of Science Citations: 19.
DE SOUZA, CAMILA FERREIRA; SABEDOT, THAIS S.; MALTA, TATHIANE M.; STETSON, LINDSAY; MOROZOVA, OLENA; SOKOLOV, ARTEM; LAIRD, PETER W.; WIZNEROWICZ, MACIEJ; IAVARONE, ANTONIO; SNYDER, JAMES; DECARVALHO, ANA; SANBORN, ZACHARY; MCDONALD, KERRIE L.; FRIEDMAN, WILLIAM A.; TIRAPELLI, DANIELA; POISSON, LAILA; MIKKELSEN, TOM; CARLOTTI, JR., CARLOS G.; KALKANIS, STEVEN; ZENKLUSEN, JEAN; SALAMA, SOFIE R.; BARNHOLTZ-SLOAN, JILL S.; NOUSHMEHR, HOUTAN. A Distinct DNA Methylation Shift in a Subset of Glioma CpG Island Methylator Phenotypes during Tumor Recurrence. CELL REPORTS, v. 23, n. 2, p. 637-651, APR 10 2018. Web of Science Citations: 21.
MALTA, TATHIANE M.; SOKOLOV, ARTEM; GENTLES, ANDREW J.; BURZYKOWSKI, TOMASZ; POISSON, LAILA; WEINSTEIN, JOHN N.; KAMINSKA, BOZENA; HUELSKEN, JOERG; OMBERG, LARSSON; GEVAERT, OLIVIER; COLAPRICO, ANTONIO; CZERWINSKA, PATRYCJA; MAZUREK, SYLWIA; MISHRA, LOPA; HEYN, HOLGER; KRASNITZ, ALEX; GODWIN, ANDREW K.; LAZAR, ALEXANDER J.; STUART, JOSHUA M.; HOADLEY, KATHERINE A.; LAIRD, PETER W.; NOUSHMEHR, HOUTAN; WIZNEROWICZ, MACIEJ; NETWORK, CANCER GENOME ATLAS RES. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell, v. 173, n. 2, p. 338+, APR 5 2018. Web of Science Citations: 92.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.