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25 results on '"Shah, Yajas"'

6. Supplementary Data T1 from Advanced Age in Humans and Mouse Models of Glioblastoma Show Decreased Survival from Extratumoral Influence

Author

Johnson, Margaret, primary, Bell, April, primary, Lauing, Kristen L., primary, Ladomersky, Erik, primary, Zhai, Lijie, primary, Penco-Campillo, Manon, primary, Shah, Yajas, primary, Mauer, Elizabeth, primary, Xiu, Joanne, primary, Nicolaides, Theodore, primary, Drumm, Michael, primary, McCortney, Kathleen, primary, Elemento, Olivier, primary, Kim, Miri, primary, Bommi, Prashant, primary, Low, Justin T., primary, Memon, Ruba, primary, Wu, Jennifer, primary, Zhao, Junfei, primary, Mi, Xinlei, primary, Glantz, Michael J., primary, Sengupta, Soma, primary, Castro, Brandyn, primary, Yamini, Bakhtiar, primary, Horbinski, Craig, primary, Baker, Darren J., primary, Walunas, Theresa L., primary, Schiltz, Gary E., primary, Lukas, Rimas V., primary, and Wainwright, Derek A., primary

Published
2023
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7. MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Author

Tasci, Erdal, Shah, Yajas, Jagasia, Sarisha, Zhuge, Ying, Shephard, Jason, Johnson, Margaret O., Elemento, Olivier, Joyce, Thomas, Chappidi, Shreya, Cooley Zgela, Theresa, Sproull, Mary, Mackey, Megan, Camphausen, Kevin, and Krauze, Andra Valentina

Subjects
*BLOOD proteins, *FEATURE selection, *O6-Methylguanine-DNA Methyltransferase, *PROTEIN expression, *GLIOBLASTOMA multiforme
Abstract

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Transcriptomic signatures of chronic active antibody-mediated rejection deciphered by RNA sequencing of human kidney allografts.

Author

Shah, Yajas, primary, Yang, Hua, additional, Mueller, Franco B., additional, Li, Carol, additional, Gul Rahim, Shab E., additional, Varma, Elly, additional, Salinas, Thalia, additional, Dadhania, Darshana M., additional, Salvatore, Steven, additional, Seshan, Surya V., additional, Sharma, Vijay K., additional, Elemento, Olivier, additional, Suthanthiran, Manikkam, additional, and Muthukumar, Thangamani, additional

Published
2023
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10. Advanced age in humans and mouse models of glioblastoma show decreased survival from extratumoral influence

Author

Johnson, Margaret, primary, Bell, April, additional, Lauing, Kristen L., additional, Ladomersky, Erik, additional, Zhai, Lijie, additional, Penco-Campillo, Manon, additional, Shah, Yajas, additional, Mauer, Elizabeth, additional, Xiu, Joanne, additional, Nicolaides, Theodore, additional, Drumm, Michael, additional, McCortney, Kathleen, additional, Elemento, Olivier, additional, Kim, Miri, additional, Bommi, Prashant, additional, Low, Justin T., additional, Memon, Ruba, additional, Wu, Jennifer, additional, Zhao, Junfei, additional, Mi, Xinlei, additional, Glantz, Michael J., additional, Sengupta, Soma, additional, Castro, Brandyn, additional, Yamini, Bakhtiar, additional, Horbinski, Craig, additional, Baker, Darren J., additional, Walunas, Theresa L., additional, Schiltz, Gary E., additional, Lukas, Rimas V., additional, and Wainwright, Derek A., additional

Published
2023
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12. BIOM-43. THE GENOMIC, TRANSCRIPTOMIC, AND EPIGENOMIC LANDSCAPE OF ISOCITRATE DEHYDROGENASE WILD TYPE GLIOBLASTOMA ACROSS THE AGE CONTINUUM

Author

Johnson, Margaret, primary, Bell, April, additional, Shah, Yajas, additional, Viets-Layng, Kayla, additional, Mauer, Elizabeth, additional, Xiu, Joanne, additional, Elemento, Olivier, additional, Glantz, Michael, additional, Walker, Phillip, additional, Chen, Clark, additional, Dunbar, Erin, additional, Fonkem, Ekokobe, additional, Kesari, Santosh, additional, Brenner, Andrew, additional, Newton, Herbert, additional, Low, Justin, additional, Sumrall, Ashley, additional, Korn, Wolfgang, additional, Ashley, David, additional, and Wainwright, Derek, additional

Published
2022
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14. Abstract 5270: Developing a predictive model for pancreatic neuroendocrine tumor metastatic potential: A multi-institutional analysis

Author

Greenberg, Jacques A., primary, Ivanov, Nikolay A., additional, Shah, Yajas, additional, Kulm, Scott, additional, Williams, Jelani, additional, Tran, Catherine G., additional, Scognamiglio, Theresa, additional, Lee, Yeon Joo, additional, Egan, Caitlin E., additional, Min, Irene M., additional, Zarnegar, Rasa, additional, Howe, James, additional, Keutgen, Xavier, additional, Fahey, Thomas J., additional, Elemento, Olivier, additional, and Finnerty, Brendan M., additional

Published
2022
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16. Genomic instability is enriched in localized prostate cancers from men of African ancestry.

Author

Nauseef, Jones T., primary, Shah, Yajas, additional, Shaiber, Alon, additional, Rosiene, Joel, additional, Wilkes, David, additional, Sigouros, Michael, additional, Manohar, Jyothi, additional, Vlachostergios, Panagiotis J., additional, Robinson, Brian D., additional, Elemento, Olivier, additional, Nanus, David M., additional, Mosquera, Juan Miguel, additional, and Imielinski, Marcin, additional

Published
2022
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21. Effects of B cell–activating factor on tumor immunity

Author

Yarchoan, Mark, primary, Ho, Won Jin, additional, Mohan, Aditya, additional, Shah, Yajas, additional, Vithayathil, Teena, additional, Leatherman, James, additional, Dennison, Lauren, additional, Zaidi, Neeha, additional, Ganguly, Sudipto, additional, Woolman, Skylar, additional, Cruz, Kayla, additional, Armstrong, Todd D., additional, and Jaffee, Elizabeth M., additional

Published
2020
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24. Developing a predictive model for metastatic potential in pancreatic neuroendocrine tumor.

Author

Greenberg JA, Shah Y, Ivanov NA, Marshall T, Kulm S, Williams J, Tran C, Scognamiglio T, Heymann JJ, Lee-Saxton YJ, Egan C, Majumdar S, Min IM, Zarnegar R, Howe J, Keutgen XM, Fahey Iii TJ, Elemento O, and Finnerty BM

Abstract

Context: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive., Objective: Use machine learning to develop predictive models of PNET metastatic potential dependent upon transcriptomic signature., Methods: RNA-sequencing data were analyzed from 95 surgically-resected primary PNETs in an international cohort. Two cohorts were generated with equally balanced metastatic PNET composition. Machine learning was used to create predictive models distinguishing between localized and metastatic tumors. Models were validated on an independent cohort of 29 formalin-fixed, paraffin-embedded samples using NanoString nCounter®, a clinically-available mRNA quantification platform., Results: Gene expression analysis identified concordant differentially expressed genes between the two cohorts. Gene set enrichment analysis identified additional genes that contributed to enriched biologic pathways in metastatic PNETs. Expression values for these genes were combined with an additional 7 genes known to contribute to PNET oncogenesis and prognosis, including ARX and PDX1. Eight specific genes (AURKA, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1, ZPLD1) were identified as sufficient to classify the metastatic status with high sensitivity (87.5% - 93.8%) and specificity (78.1% - 96.9%). These models remained predictive of the metastatic phenotype using NanoString nCounter® on the independent validation cohort, achieving a median AUROC of 0.886., Conclusions: We identified and validated an eight-gene panel predictive of the metastatic phenotype in PNETs, which can be detected using the clinically-available NanoString nCounter® system. This panel should be studied prospectively to determine its utility in guiding operative versus non-operative management., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. See the journal About page for additional terms.)

Published
2024
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25. FORMATION OF MALIGNANT, METASTATIC SMALL CELL LUNG CANCERS THROUGH OVERPRODUCTION OF cMYC PROTEIN IN TP53 AND RB1 DEPLETED PULMONARY NEUROENDOCRINE CELLS DERIVED FROM HUMAN EMBRYONIC STEM CELLS.

Author

Chen HJ, Gardner EE, Shah Y, Zhang K, Thakur A, Zhang C, Elemento O, and Varmus H

Abstract

We recently described our initial efforts to develop a model for small cell lung cancer (SCLC) derived from human embryonic stem cells (hESCs) that were differentiated to form pulmonary neuroendocrine cells (PNECs), a putative cell of origin for neuroendocrine-positive SCLC. Although reduced expression of the tumor suppressor genes TP53 and RB1 allowed the induced PNECs to form subcutaneous growths in immune-deficient mice, the tumors did not display the aggressive characteristics of SCLC seen in human patients. Here we report that the additional, doxycycline-regulated expression of a transgene encoding wild-type or mutant cMYC protein promotes rapid growth, invasion, and metastasis of these hESC-derived cells after injection into the renal capsule. Similar to others, we find that the addition of cMYC encourages the formation of the SCLC-N subtype, marked by high levels of NEUROD1 RNA. Using paired primary and metastatic samples for RNA sequencing, we observe that the subtype of SCLC does not change upon metastatic spread and that production of NEUROD1 is maintained. We also describe histological features of these malignant, SCLC-like tumors derived from hESCs and discuss potential uses of this model in efforts to control and better understand this recalcitrant neoplasm.

Published
2023
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