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G. Courthod
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P1.06 - Poster Session/ Screening and Early Detection (ID 218)
- Event: WCLC 2015
- Type: Poster
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/07/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P1.06-009 - Volatolomic Signatures to Assess Sensitivity to FGFR Tyrosine Kinase Inhibitors (ID 1711)
09:30 - 09:30 | Author(s): G. Courthod
- Abstract
Background:
Targeted therapy is transforming the treatment of lung cancer. Such therapies are critically dependent on companion diagnostics that can predict the response to therapy. An ideal test is one that is quick, inexpensive, and non-invasive. In this regard, artificial intelligence nanosensor-based devices that profile volatolomic signatures (through volatile organic compounds (VOCs) analysis) have shown exciting potential. Numerous studies have shown cancer cells produce characteristic patterns of VOCs as a byproduct of their metabolism. These patterns can be used to diagnose patients with cancer using exhaled-breath samples. Here we asked whether the VOC patterns emanating from cancer cells could also be used to guide targeted therapy. In particular, we investigated whether lung cancer cell lines known to be sensitive to FGFR tyrosine kinase inhibitors (TKIs) can be distinguished from cell lines known to be resistant using an array of cross reactive, highly sensitive chemiresistors composed of gold nanoparticles (GNP) and carbon nanotubes (CNTs) coated with various recognition layers previously shown to be highly effective at profiling VOCs.
Methods:
Fourteen sensitive cell lines having an IC~50~ ≤ 50 nM for Ponatinib and AZD4547 (nonspecific and specific FGFR TKIs, respectively) and 21 resistant cell lines representing small cell and non-small cell lung cancers were cultured in complete media (RPMI 1640, 10% fetal bovine serum, and penicillin/streptomycin) under standard conditions to 50% to 75% confluency. SKC Tenax® TA Adsorbent resin was used to collect the VOCs from the head space of each cell line over a period of 60 to 72 hours. Triplicate measures were collected on each sample along with biological replicates. VOCs were also collected at the same time from control plates containing media only. After thermal desorption, the VOC pattern of each sample was characterized using a chemiresistor array of 36 sensors and 4 features per sensor. A statistical pattern recognition analysis was then conducted using a discriminant function analysis (DFA) algorithm to identify the most informative sensors and features.
Results:
We found that sensitive cell lines could be distinguished from resistant cell lines using only 4 sensors and one feature from each (GNP+dodecanethiol, CNT+PAH, GNP+thiol and CNT+β dextrin). Leave-one-out cross validation indicated a sensitivity of 88% for the FGFR TKI-sensitive cell lines with 100% specificity and 92% accuracy. The area under the receiver-operating characteristic curve was 70% and Wilcoxon p-value of 0.06.
Conclusion:
Profiling the VOCs emanating from lung cancer cells shows excellent diagnostic potential as a means of gauging initial sensitivity to FGFR1 TKIs. Consequently, this study suggests that the electronic nose devices currently being developed to profile exhaled breath for cancer detection could also play an important role in predicting responses to targeted therapies. Although cell lines are useful for identifying the VOC pattern that predicts the cancer cell response to therapy, they do not necessarily reflect the complexity that occurs in vivo due to interactions with the microenvironment. Therefore, future studies are needed to confirm if these results can be extended to project efficacy in patients assigned to FGFR TKI therapy.
