« Back
Mechanism of Treatment-induced Drug Resistance in Lung Cancer
Poster Title: Mechanism of Treatment-induced Drug Resistance in Lung Cancer
Submitted on 24 Feb 2017
Author(s): Mark Howell a,b*, Ryan Green a,b, Eva Samal a,b, Rajesh Nair d,g, Stanley Stevens f, Jit Banerjee a, Shyam Mohapatra b,c,d,e, and Subhra Mohapatra a,b,e
Affiliations: a Molecular Medicine Department, b Center for Research & Education in Nanobioengineering, c Division of Translational Medicine, Internal Medicine, Morsani College of Medicine, d College of Pharmacy, University of South Florida, e James A Haley Veterans Hospital, f Cell Biology, Microbiology, and Molecular Biology, College of Arts and Sciences, g Transgenex Nanobiotech Inc., Tampa, FL 33613
This poster was presented at USF Research Day 2017
Poster Views: 833
View poster »

Poster Information
Abstract: Objectives: Lung cancer is the number one cause of cancer related death in both males and females. About 20% of all non-small cell lung cancer (NSCLC) patients are expected to harbor an Epidermal Growth Factor Receptor (EGFR) activating mutation. EGFR inhibitors have been shown to provide clinical benefits over chemotherapy for lung cancer patients with EGFR activating mutations. First- and second-generation EGFR tyrosine kinase inhibitors (TKIs) are clinically approved to treat advanced NSCLC patients. However, despite the initial clinical responses to these EGFR targeted therapies, long-term efficacy is not possible because acquired drug resistance hampers the effectiveness of these therapies. However, despite this gradually increasing knowledge, many routes are yet to be discovered. We found that a polymeric nanofibrous scaffold platform established in our laboratory allows growth of three-dimensional (3D) tumor-like aggregates (referred to as tumoroids), which resemble in vivo tumors. Tumoroids exhibit better drug resistance compared to two-dimensional (2D) cultures that lack ability to mimic the environment of the tumor microenvironment. We hypothesize that 3D scaffold will allow us elucidate EGFR TKI resistance mechanisms and help design more efficient treatment strategies to block resistance.

Methods: To investigate drug resistance to EGFR TKIs, we have developed lapatinib resistant human lung cancer cell lines as model for de novo drug resistance. To derive drug resistant (DR) cells, H1975 human lung cancer cells, that carry L858R and T790M point mutations in exons 20 and 21 of EGFR, were grown in a static concentration of lapatinib for up to 30 days. We have also developed EGFR TKI resistant H1650 and H1299 human lung cancer cell lines to confirm any potential findings in cell lines with different EGFR mutations thus enhancing the reproducibility and relevance of the work. The drug sensitivity of the parental and DR cells on both the monolayer and the 3D scaffold were determined by testing a panel of standard-of-care chemotherapeutics along with EGFR TKIs. To determine the mechanism of resistance, mass spectroscopy was used to determine the relative levels of certain key proteins being expressed in the H1975 and lapatinib DR-H1975 cell lines. The proteomics data collected using Mass Spec is meant to serve as a guide for future experimentation and all results of intriguing proteins are confirmed and validated in our EGFR TKI resistant models using other methods, including western blotting and mRNA transcript analysis.

Results: A comparison of the drug sensitivity showed that parental cells were at least 3- fold more sensitive to the EGFR TKIs compared to the DR cells. The sensitivity to EGFR TKIs was further decreased when DR cells were cultured on our fibrous 3D scaffold. Furthermore, qPCR analysis showed upregulation of certain cancer stem cell markers, such as OCT-4 and Nanog, when the cells were grown on the 3D scaffold. This may explain the increased resistance seen when the cells are grown on the 3D scaffold. EGFR TKI resistant cells were also resistant to other anti-cancer agents, such as taxol and gemcitabine. Data mining the significantly differentially expressed proteins list generated by the mass spectroscopic analysis revealed that the protein expression is skewed in lapatinib DR-H1975 cell line as compared to the H1975 cell line. Proteins involved in all aspects of homeostasis including metabolism, biosynthesis and oxidative regulation were significantly up regulated in the lapatinib resistant cell line. These proteins are currently being validated at the functional level. .

Conclusions: By studying the cellular effects on viability and cell death of acquired EGFR TKI resistance, as well as the mechanism through which this resistance is maintained can help to better understand how lung cancer cells become drug resistant to EGFR TKIs and help strategize potential ways of overcoming this resistance.
Summary: Drug resistance is common in NSCLC patients receiving treatment with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs). To investigate the development of this drug resistance, our lab has developed EGFR TKI resistant human lung cancer cell lines. Preliminary data has revealed that upregulation of 2 proteins that may be causing enhanced EGFR downstream signaling, possibly outcompeting the effects of the inhibitor and leading to resistance. References: Research Supported by: R01CA152005, HHSN261201400022C, HHSN261201400028C. Report abuse »
Ask the author a question about this poster.
Ask a Question »

Creative Commons

Related Posters

VITVO: Mimicking In Vivo Complexity By The Innovative 3D Model
Olivia Candini1, Giulia Grisendi1, Elisabetta Manuela Foppiani1, Matteo Brogli1, Beatrice Aramini2, Valentina Masciale3, Carlotta Spano1, Tiziana Petrachi4, Elena Veronesi4, Pierfranco Conte5,6, Giorgio Mari1 & Massimo Dominici1,3

Helana Lutfi and Shaban Nuredini

Applying Trapped Ion Mobility Separation (TIMS) in combination with Parallel Accumulation Serial Fragmentation (PASEF) for analysis of lipidomics samples
Sebastian Götz1, Sven W Meyer1,Ulrike Schweiger-Hufnagel1,Aiko Barsch1, Ningombam Sanjib Meitei2

Characterization of patient-derived organoids cultured on a gas-rich, liquid-liquid interface
James T. Shoemaker, Katherine R. Richardson, Jamie Arnst, Adam Marcus, Jelena Vukasinovic

Machine Learning: A New Breakthrough in Medical Diagnosis
Punitha Mahendran, Anis Joelaira, Chai Yong Chia, Fazidatul Aziz, Hasyimah Emran, Siew Fun Lee, Wai Kit Wong, Yee Yan Tang, Li Yang Wong