Pradeep Kumar Gupta

Dr. Pradeep Kumar Gupta

University of Pennsylvania Perelman School of Medicine
orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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2 February 2021 in Journal of Neuro-Oncology
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Journal Article
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MTMickael TORDJMAN
Mickael TORDJMAN
GMGuillaume Madelin
Guillaume Madelin
PGPradeep Kumar Gupta
Pradeep Kumar Gupta
CCChristine Cordova
Christine Cordova
SKSylvia C. Kurz
Sylvia C. Kurz
RJRajan Jain
Rajan Jain

10 December 2013 in Neuroradiology
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Journal Article
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PGPradeep Kumar Gupta
Pradeep Kumar Gupta
RGRavindra Kumar Garg
Ravindra Kumar Garg
RGRakesh Kumar Gupta
Rakesh Kumar Gupta
HMHardeep Singh Malhotra
Hardeep Singh Malhotra
VPVimal Kumar Paliwal
Vimal Kumar Paliwal
CPChandra Mani Pandey
Chandra Mani Pandey

Verified
Journal Article
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SCSanjeev Chawla
Sanjeev Chawla
VSVanessa Shehu
Vanessa Shehu
PGPradeep Kumar Gupta
Pradeep Kumar Gupta
KNKavindra Nath
Kavindra Nath
HPHarish Poptani
Harish Poptani
Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient’s immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them.

30 September 2017 in Journal of Magnetic Resonance Imaging
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LGLalit Gupta
Lalit Gupta
RGRakesh K. Gupta
Rakesh K. Gupta
APAlida A. Postma
Alida A. Postma
PSPrativa Sahoo
Prativa Sahoo
PGPradeep Kumar Gupta
Pradeep Kumar Gupta
WBWalter H. Backes
Walter H. Backes

1 October 2016 in Magnetic Resonance Imaging
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Journal Article
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PSPrativa Sahoo
Prativa Sahoo
PGPradeep Kumar Gupta
Pradeep Kumar Gupta
AAAshish Awasthi
Ashish Awasthi
CPChandra M. Pandey
Chandra M. Pandey
RPRana Patir
Rana Patir
RGRakesh K Gupta
Rakesh K Gupta

15 January 2024 in Cancers
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Journal Article
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PGPradeep Kumar Gupta
Pradeep Kumar Gupta
SOStepan Orlovskiy
Stepan Orlovskiy
FAFernando Arias-Mendoza
Fernando Arias-Mendoza
DNDavid Nelson
David Nelson
Aria OsborneAria Osborne
Aria Osborne
KNKavindra Nath
Kavindra Nath
Dabrafenib therapy for metastatic melanoma focuses on blocking growth-promoting signals produced by a hyperactive BRAF protein. We report the metabolic differences of four human melanoma cell lines with diverse responses to dabrafenib therapy (30 mg/kg; oral): WM3918 < WM9838BR < WM983B < DB-1. Our goal was to determine if metabolic changes produced by the altered signaling pathway due to BRAF mutations differ in the melanoma models and whether these differences correlate with response to treatment. We assessed metabolic changes in isolated cells using high-resolution proton magnetic resonance spectroscopy (1H MRS) and supplementary biochemical assays. We also noninvasively studied mouse xenografts using proton and phosphorus (1H/31P) MRS. We found consistent changes in lactate and alanine, either in isolated cells or mouse xenografts, correlating with their relative dabrafenib responsiveness. In xenografts, we also observed that a more significant response to dabrafenib correlated with higher bioenergetics (i.e., increased βNTP/Pi). Notably, our noninvasive assessment of the metabolic status of the human melanoma xenografts by 1H/31P MRS demonstrated early metabolite changes preceding therapy response (i.e., tumor shrinkage). Therefore, this noninvasive methodology could be translated to assess in vivo predictive metabolic biomarkers of response in melanoma patients under dabrafenib and probably other signaling inhibition therapies.