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Author Notes:

Nicholas R. Powell, mockpowe@iu.edu

Joseph Ipe, joseph.ipe@10xgenomics.com

Nicholas R. Powell: Conceptualization (supporting); data curation (lead); formal analysis (lead); methodology (supporting); writing – original draft (equal); writing – review and editing (equal). Rebecca M. Silvola: Conceptualization (supporting); data curation (supporting); formal analysis (equal); methodology (supporting); writing – original draft (supporting); writing – review and editing (supporting). John S. Howard: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); methodology (supporting); writing – original draft (supporting); writing – review and editing (supporting). Sunil Badve: Conceptualization (supporting); data curation (equal); formal analysis (supporting); methodology (supporting); writing – original draft (supporting); writing – review and editing (supporting). Todd C. Skaar: Conceptualization (equal); data curation (supporting); formal analysis (supporting); funding acquisition (equal); methodology (supporting); writing – original draft (supporting); writing – review and editing (supporting). Joseph Ipe: Conceptualization (lead); data curation (equal); formal analysis (equal); funding acquisition (equal); methodology (lead); writing – original draft (equal); writing – review and editing (equal).

Sequencing analysis was carried out in the Center for Medical Genomics at Indiana University School of Medicine. Biospecimens were stored in the Clinical and Translational Sciences Institute (CTSI) Specimen Storage Facility which is supported, in part, by grant NIH/NCRR RR020128. Biospecimens were obtained with support from the Indiana CTSI funded, in part by Award Number UL1TR002529 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Joseph Ipe began working at 10× Genomics after this study was completed, and while no conflict of interest existed during the study, this statement is provided for full disclosure. None of the other authors have potential conflicts of interest related to this work.

Subject:

Research Funding:

This work was supported by NIH‐NIGMS grants: T32GM008425 (N.R.P.), and T32GM842528 (R.M.S.), the Vera Bradley Foundation for Breast Cancer Research (J.I.), and the Indiana University Grand Challenge Precision Health Initiative (J.I.).

Keywords:

  • chemotherapy
  • pharmacogene
  • resistance
  • spatial
  • transcriptomics
  • Female
  • Humans
  • Breast
  • Breast Neoplasms
  • Gene Expression Profiling
  • Transcriptome

Quantification of spatial pharmacogene expression heterogeneity in breast tumors

Tools:

Journal Title:

Cancer Reports

Volume:

Volume 6, Number 1

Publisher:

, Pages e1686-e1686

Type of Work:

Article | Final Publisher PDF

Abstract:

Background: Chemotherapeutic drug concentrations vary across different regions of tumors and this is thought to be involved in development of chemotherapy resistance. Insufficient drug delivery to some regions of the tumor may be due to spatial differences in expression of genes involved in the disposition, transport, and detoxification of drugs (pharmacogenes). Therefore, in this study, we analyzed the spatial expression of 286 pharmacogenes in six breast cancer tissues using the recently developed Visium spatial transcriptomics platform to (1) determine if these pharmacogenes are expressed heterogeneously across tumor tissue and (2) to determine which pharmacogenes have the most spatial expression heterogeneity. Methods and Results: The spatial transcriptomics technology sequences the transcriptome of 55 um diameter barcoded sections (spots) across a tissue sample. We analyzed spatial gene expression profiles of four biobank-sourced breast tumor samples in addition to two breast tumor sample datasets from 10× Genomics. We define heterogeneity as the interquartile range of read counts. Collectively, we identified 8887 spots in tumor regions, 3814 in stroma, 44 in lymphocytes, and 116 in normal regions based on pathologist annotation of the tissues. We showed statistically significant differences in expression of pharmacogenes in tumor regions compared to surrounding non-tumor regions. We also observed that the most heterogeneously expressed genes within tumor regions were involved in reactive oxygen species (ROS) handling and detoxification mechanisms. GPX4, GSTP1, MGST3, SOD1, CYP4Z1, CYB5R3, GSTK1, and NAT1 showed the most heterogeneous expression within tumor regions. Conclusions: The heterogeneous expression of these pharmacogenes may have important implications for cancer therapy due to their ability to impact drug distribution and efficacy throughout the tumor. Our results suggest that chemoresistance caused by expression of GPX4, GSTP1, MGST3, and SOD1 may be intrinsic, not acquired, since the heterogeneity is not specific to chemotherapy-treated samples or cell type. Additionally, we identified candidate chemoresistance pharmacogenes that can be further tested through focused follow-up studies.

Copyright information:

© 2022 The Authors. Cancer Reports published by Wiley Periodicals LLC.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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