A new study has demonstrated competitive exclusion in gefitinib resistant and sensitive ancestor lung cancers, providing a better understanding of how drug resistance develops.
Published in Science Advances, the work identified the mutations that facilitate gefitinib resistance using whole-exome and RNA sequencing.
Conducted by researchers at the Department of Translational Haematology and Oncology Research at the Cleveland Clinic and CWRM School of Medicine, the new research lends support to the concept of treatment holidays as a way to counter treatment resistant non-small cell lung cancer.
Competitive release
Competitive release is a theorised driver of treatment resistance. In tumours, it is thought that the selective killing of sensitive cells during therapy removes competitive restrictions on resistant populations. This allows for their outgrowth and subsequent therapeutic failure. This has been observed in bacteria and parasites but not yet in cancer, which this study aimed to rectify.
As a population becomes increasingly resistant to a treatment, it is common for that population to pay a “fitness cost” to maintain that resistant mechanism, leading to a reduced growth rate when compared to the ancestor from which it was derived.
This has led many researchers to suggest that the sensitive ancestor is likely to outcompete the resistant clone when selection is removed. Therefore, treatment holidays may be beneficial to the maintenance of a treatable cancer population.
Gefitinib resistant cells
Cells resistant to the metastatic non-small cell lung cancer treatment gefitinib were derived from existing lung cancer cells by continual treatment with gefitinib over six months, and grown in an in vitro co-culture experiment with their sensitive ancestors.
The authors measured frequency-dependent growth rates for a gefitinib-resistant population and the ancestor from which it was derived. The study found that there was a substantial growth cost to the resistant phenotype. It grew at three-fourths the rate of the sensitive population.
The experiments revealed that the resistant population was outcompeted by the ancestral line at all studied population frequencies in the absence of therapy, pointing to complete competitive exclusion of the resistant population and a cost of resistance. When gefitinib was added, there was a complete reversal of this effect, and the resistant clone was able to outcompete the sensitive ancestor.
Mutation identification
To characterise molecular mechanisms that facilitate gefitinib resistance, the researchers then performed whole-exome and RNA sequencing, comparing resistant cells with parental and sensitive ones.
They observed that resistant cells harboured clonal KRAS G12D mutation, a strong oncogenic driver. It has previously been shown to cause strong gefitinib resistance.
In addition, they found that resistant cells displayed several copy number gains and losses that have been previously linked to lung cancer progression and therapy resistance.
New doors for treatment resistant disease
With approximately 90% of cancer deaths attributed to treatment-resistant disease, these cellular interactions have high stakes.
Furthermore, the findings opposed the maximal tolerable dose hypothesis, finding that higher drug doses may not inhibit tumour burden better than lower doses.
Measuring frequency-dependent ecological interactions is extremely challenging. Acknowledging the study’s limitations, the authors responded, “In the future, we hope to measure many replicate populations to several drugs to explore the repeatability of these interactions.”
These results highlight the importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and translating adaptive therapy regimens to the clinic.
Written by Poppy Jayne Morgan, Front Line Genomics
Image Credit: Canva
