text.skipToContent text.skipToNavigation
Bulk Order
If not, click 'cancel'. You can also save this item for later.
Cancel
If not, click 'cancel'. You can also save this item for later.
Cancel

Unravelling EGF receptor drug resistance in lung cancer with single cell sequencing

no image

 

New research using the single cell RNA sequencing technique shines a powerful light on the huge diversity of cells within a tumour, which provide multiple mechanisms for evasion of treatment through drug resistance. Could single cell RNA sequencing techniques help scientists design a new generation of more effective targeted therapies?

 

Lung cancer causes more deaths worldwide than breast and prostate cancer combined, and has the worst five-year survival rate of all the common cancers at 22 per cent. Mutations that activate growth factor receptors are common in lung cancer and correlate with a poor prognosis.  Immediate, “first-line” treatment of patients with activating mutations in the  epidermal  growth factor receptor (EGFR) using tyrosine kinase inhibitors (TKIs) has proven one of the most successful lung cancer targeted therapies to date.  However, despite good initial patient responses to treatment with EGFR-TKIs such as erlotinib and gefitinib, most go on to develop resistance leading to tumour regrowth.

 

Single target therapies are rarely successful because cells accumulate further cancerous changes that enable signalling pathways to bypass the drug blockage. Combination therapies - consisting of drugs targeting multiple cancer targets in the cell - represent a large step forward and boost survival rates, although drug resistance remains a significant issue leading to relapse in most patients.

 

Understanding the early changes that occur in individual cells at the start of disease progression helps significantly in dissecting the mechanisms via which tumours can bypass inhibitor drugs, guiding scientists in the design of effective combination therapies. However, a significant complicating factor lies in the vast diversity of tumours, which contain mixed populations of cells, and are capable of employing different mechanisms to evade treatment. By generating a single readout from biopsy samples at a single moment, most studies fail to capture the order of events leading to drug resistance, as well as the clinically important diversity that exists between different cells in the tumour.

 

A 2021 paper in Nature Communications by Professor Elizaveta Benevolenskaya’s lab at the University of Chicago addresses both questions in an elegant way. In this study, an established non-small-cell lung cancer preclinical ‘xenograft’ model was used in which lung cancer cell lines including HCC827and H1975 cells were injected into mice, leading to formation of tumours, which were then treated with EGFR-TKI therapeutics erlotinib and osimertinib.  Mirroring patient responses, tumors shrank sharply, followed by relapse triggered by the regrowth of drug resistant cell subpopulations.

 

Using single cell RNA sequencing to analyse the resistant cell colonies, Benevolenskaya’s group discovered a wide range of distinct signatures associated with individual cells, many of which were previously unassociated with drug resistance.  Common among the drug resistant traits were an increased ability to break down drugs, as well as “epithelial to mesenchymal transition” characteristics - in which cells start to undergo a change from one cell type into another, increasing the chances of metastasis.

 

Benevolenskaya’s study identified a subpopulation of cells found to have developed a clinically relevant chromosomal rearrangement, leading to production of the cancer promoting mutant protein ALK-kinase.  This information was used to formulate a combination treatment approach: targeting cells with the ALK inhibitor crozotinib, together with the EGF receptor inhibitor erlotinib, which lead to successful eradication of the cancer cells.

 

This groundbreaking study shows how the powerful technique of single cell RNA sequencing can be used formulate a detailed map of cellular changes associated with cancer disease progression, capturing the huge diversity of individual drug resistant cells within a tumour. Most impressively, cancer cell drug resistance signatures were characterised in this study and then used to design combination therapies that proved to be effective in combatting drug resistance.  However, clinical work is required to explore the true potential of this novel technology and single-cell technologies are not immediately available in the clinical oncology setting. Writing in Nature Communications, Professor Benevolenskaya concludes on a hopeful note: “Since we can link each cell with mechanism-related changes, we envision using this type of data to help guide screening and targeting strategies”. 

 

Looking for Bioactive Molecules for Cancer Research and Drug Discovery?

 

Toronto Research Chemicals offer a large and unique collection of >4500 in stock Research Chemicals, including bioactive molecules, stable labelled isotopes, protein kinase modulators, apoptosis inducers and angiogenesis inhibitors.

 

Looking for in vitro models for Cancer Research and Drug Discovery?

 

ATCC offer a unique collection of Cancer Research Tools including >4000 Cancer Cell Lines, in addition to wide ranging advanced models such as Patient Derived Cancer Organoids,  EMT/MET Reporter Cell Lines and Luciferase-labeled Cells.

 

>>Discover our Oncology range

 

Custom request

Need something you can't find on our website or need to discuss a specific project? Get in touch with us by filling the following form.

 

>>Fill the form

 

Note on the author

Joe Lackey holds a PhD in Cell Signalling and Cancer from the University of Dundee and has worked as an ATCC Product Manager for 2 years. Joe is writing this series of blogs to explore his significant interest in the role in vitro models have to play in drug discovery, within the greater context of advances in precision medicine and the journey to the clinic. 

 

Previous blogs by the same author

Fighting Drug Resistance in Colorectal Cancer with a Triple Attack!

Novel approaches to Parkinson’s bring a new hope

Can 3D models solve the predictability crisis in renal toxicology?

Punchout session timeout warning

Your punchout session will expire in1 min59 sec.

Select "Continue session" to extend your session.