Utilization of Discarded Surgical Tissue from Ultrasonic Aspirators to Establish Patient‐Derived Metastatic Brain Tumor Cells: A Guide from the Operating Room to the Research Laboratory

Abstract

Patient‐derived cells from surgical resections are of paramount importance to brain tumor research. It is well known that there is cellular and microenvironmental heterogeneity within a single tumor mass. Thus, current established protocols for propagating tumor cells in vitro are limiting because resections obtained from conventional singular samples limit the diversity in cell populations and do not accurately model the heterogeneous tumor. Utilization of discarded tissue obtained from cavitron ultrasonic surgical aspirator (CUSA) of the whole tumor mass allows for establishing novel cell lines in vitro from the entirety of the tumor, thereby creating an accurate representation of the heterogeneous population of cells originally present in the tumor. Furthermore, while others have described protocols for establishing patient tumor lines once tissue has arrived in the research lab, a primer from the operating room (OR) to the research lab has not been described before. This is integral, as basic research scientists need to understand the surgical environment of the OR, including the methods utilized to obtain a patient's tumor resection, in order to more accurately model cancer biology in laboratory. © 2021 Wiley Periodicals LLC.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2021
Source ID
10.1002/cpz1.140

Entities

People

  • Casey Jarvis
  • Edith Yuan
  • Frances Chow
  • Frank J Attenello
  • Gabriel Zada
  • Josh Neman
  • Krutika Deshpande
  • Michelle Lin
  • Steven L Giannotta
  • Thomas C. Chen
  • Vahan Martirosian

Organizations

  • Keck School of Medicine of USC
  • National Cancer Institute
  • National Institutes of Health
  • United States Department of Defense
  • University of Southern California

Tags

Fields of Study

  • Medicine

Readers

  • Oncology
  • Oncology and Biomarker-Based Cancer Detection.
  • Systems Analysis and Design