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T. Hinz
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P2.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 234)
- Event: WCLC 2015
- Type: Poster
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P2.04-006 - MiRNA Signature to Assess Sensitivity to FGFR Tyrosine Kinase Inhibitors (ID 1717)
09:30 - 09:30 | Author(s): T. Hinz
- Abstract
Background:
Increased signaling through the FGF/FGFR signaling pathway has been implicated as a driver in a number of different malignancies including lymphomas, prostate cancer, breast cancer, and lung cancer. This pathway also appears to play a role in conferring de novo and acquired resistance to cancers driven by EGFR mutations. Consequently, drugs that inhibit FGFRs are being investigated as potential therapeutics for cancer. Here we screened a large panel of miRNAs as potential predictors of sensitivity to FGFR tyrosine kinase inhibitors (TKIs).
Methods:
A panel of 377 miRNAs (Megaplex Card A, Life Technologies) was screened for expression level differences between four lung cancer cell lines that are sensitive (IC~50~< 50 nM) and four lines that are resistant (IC~50~ > 100 nM) to ponatinib (non-specific FGFR TKI) and AZD4547 (FGFR-specific TKI). Expression levels were assayed by RT-qPCR and analyzed using the Statistical Analysis of Microarrays (SAM) method. Thirty-nine miRNAs having an estimated false discover rate (FDR) of zero and large median fold differences (> 8) between the sensitive and resistant lines were selected for signature development. RT-qPCR assays were incorporated into a custom microfluidics card (Life Technologies), which was used to profile the original 8 cell lines and 10 additional sensitive lines and 16 additional resistant lines (34 lines total). Logistic regression was then used to identify the best signature panel for distinguishing sensitive cell lines from resistant.
Results:
Univariate analysis indicated three miRNAs (let-7c, miR-338, and miR-218) that differed between the sensitive and resistant lines at p < .05. The best signature panel consisted of let-7c, miR-200a and miR-200b, which gave an area under the receiver operator characteristic (AUROC) curve of 0.90 (95% CI = 0.8 to 1). This performance was nearly as good as using FGFR1 mRNA alone (AUROC = 0.94). The predominant miRNA in our 3-miRNA signature was let-7c, which also exhibited a suggestive additive effect to using FGFR1 as a biomarker (p = 0.09). We also tested whether cell lines with high sensitivity to ponatinib can be made resistant by reducing the high level of let-7c in these lines. We have found that transient transfection of let-7c silencing RNA (Life Technologies) produces a decrease in FGFR1 mRNA levels for some cell lines but not others.
Conclusion:
It appears possible to predict sensitivity to an FGFR1 inhibitor using miRNA expression signatures. More studies, however, are needed to confirm the 3-marker signature developed in this study. Modulating let-7c, the predominant predictor within the signature, appears to modulate FGFR1 levels in a manner consistent with altering ponatinib sensitivity. This effect is most likely indirect as the mRNA of FGFR1 does not contain predicted binding sites for let-7c.
