Author

Sandra D. Scherer

University of UtahORCID 0000-0002-7943-8595Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Cancer Biology· 2 Bioengineering· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Research Focus:drug response / drug discovery· 3 Approach:experimental assay· 3 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1

Frequent Co-Authors

Alana L. WelmiD· 3 shared Bryan E. WelmiD· 3 shared Chieh‐Hsiang Yang· 2 shared Emilio Cortes-SancheziD· 2 shared Aaron E. AtkinsoniD· 1 shared Abhilash DeoiD· 1 shared Adriana Cristina RodrigueziD· 1 shared Barbara J. GravesiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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3 shared papersCancer Biology

Alana L. WelmORCID

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Author profile →

4 papers

Recent·Most cited

The coumarin derivative X6632 is a pan-ID protein inhibitor that suppresses tumor growth by targeting cancer cells and the tumor-associated microvasculature

Lea S. Götz, Abhilash Deo, Sandra D. Scherer, Luigi D’Antonio, Hanna T. Weber, Georg Sedlmeier, Landy Paola Torre Flores, Ulrike Kaiser, Elian Far, Ziv Raviv, Wilko Thiele, Sonja Thaler, Nicole Jung, Stefan Bräse, Caroline S. Hill, , Jonathan Paul Sleeman

bioRxiv · Aug 5, 2026 · PreprintCancer Biology

Determination of Drug Sensitivity in Patient Derived Models of Breast Cancer by Multiparametric QPI

Edward R. Polanco, Tarek E. Moustafa, Ozlen Balcioglu, Sandra D. Scherer, Emilio Cortes-Sanchez, Sophie Remick, Benjamin T. Spike, Alana L. Welm, Bryan E. Welm, Philip Seth Bernard, Thomas A. Zangle

This paper demonstrates that multiparametric QPI can assess drug sensitivity in patient-derived breast cancer models, including cryopreserved samples, revealing cellular heterogeneity and resistance mechanisms.

bioRxiv · Oct 1, 2025 · PreprintBioengineering

A precision oncology-focused deep learning framework for personalized selection of cancer therapy

Casey Sederman, Chieh‐Hsiang Yang, Emilio Cortes-Sanchez, Tonya DiSera, Xiaomeng Huang, Sandra D. Scherer, Ling Zhao, Zhengtao Chu, Eliza R. White, Aaron E. Atkinson, Jadon Wagstaff, Katherine Elena Varley, Michael T. Lewis, Yi Qiao, Bryan E. Welm, , Gábor Marth

This paper introduces ScreenDL, a deep learning model that predicts effective cancer therapies using tumor omic and drug screening data, outperforming existing methods in preclinical trials.

bioRxiv · Dec 12, 2024 · PreprintCancer Biology

ETV4 is necessary for estrogen signaling and growth in endometrial cancer cells

Adriana Cristina Rodriguez, Jeffery M. Vahrenkamp, Kristofer C. Berrett, Kathleen A. Clark, Katrin P. Guillen, Sandra D. Scherer, Chieh‐Hsiang Yang, Bryan E. Welm, Margit Maria Janat-Amsbury, Barbara J. Graves, Jason Gertz

This study shows ETV4 is essential for estrogen signaling and growth in endometrial cancer cells by enabling ER binding and nuclear translocation.

Cancer Research · Feb 11, 2020Peer-reviewedMolecular Biology