Related Experiment Video
Updated: Feb 3, 2026
Injection of Syngeneic Murine Melanoma Cells to Determine Their Metastatic Potential in the Lungs
Published on: May 24, 2016
Extracellular microvesicle microRNAs as predictive biomarkers for targeted therapy in metastastic cutaneous malignant
Fernanda Costa Svedman1,2, Warangkana Lohcharoenkal3, Matteo Bottai4
1Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Background:
Mitogen activated-protein kinase pathway inhibitors (MAPKis) improve treatment outcome in patients with disseminated BRAFV600 mutant cutaneous malignant melanoma (CMM) but responses are of limited duration due to emerging resistance. Although extensive research in mechanisms of resistance is being performed, predictive biomarkers for durable responses are still lacking. We used miRNA qPCR to investigate if different levels of extracellular microvesicle microRNA (EV miRNA) in matched plasma samples collected from patients with metastatic IV BRAFV600 mutated CMM before, during and after therapy with MAPKis could serve as predictive biomarkers.
Materials And Methods:
EV miRNAs were extracted from plasma samples from 28 patients collected before and during therapy, measured by quantitative PCR-array and correlated to therapy outcome.
Results:
Increased levels of EV let-7g-5p during treatment compared to before treatment (EV let-7g-5p_delta) were associated with better disease control with MAPKis (odds ratio 8568.4, 95% CI = 4.8-1.5e+07, P = 0.000036). Elevated levels of EV miR-497-5p during therapy were associated with prolonged progression free survival (PFS) (hazard ratio = 0.27, 95% CI = 0.13-0.52, P <0.000061).
Conclusions:
EV miRNAs let-7g-5p and miR-497-5p were identified as putative novel predictive biomarkers of MAPKi treatment benefit in metastatic CMM patients highlighting the potential relevance of assessing EV miRNA during and after treatment to unravel novel mechanisms of resistance.
Insights
Extracellular microvesicle microRNAs (EV miRNAs) let-7g-5p and miR-497-5p show promise as predictive biomarkers for durable responses to mitogen activated-protein kinase inhibitors (MAPKis) in metastatic melanoma. Assessing EV miRNAs may help understand resistance mechanisms.
Area of Science:
- Oncology
- Molecular Biology
- Biomarker Discovery
Background:
- Mitogen activated-protein kinase inhibitors (MAPKis) improve outcomes for BRAFV600 mutant cutaneous malignant melanoma (CMM).
- Treatment response duration is limited by resistance, and predictive biomarkers for durable responses are lacking.
- Extracellular microvesicle microRNAs (EV miRNAs) are being investigated as potential biomarkers.
Purpose of the Study:
- To investigate if EV miRNA levels in plasma can predict durable responses to MAPKi therapy in metastatic BRAFV600 mutated CMM patients.
- To identify specific EV miRNAs that correlate with treatment outcomes and progression-free survival.
Main Methods:
- Plasma samples were collected from 28 metastatic CMM patients before, during, and after MAPKi therapy.
- Extracellular microvesicle microRNAs (EV miRNAs) were extracted and measured using quantitative PCR-array.
- EV miRNA levels were correlated with treatment outcomes, including disease control and progression-free survival.
Main Results:
- Increased EV let-7g-5p levels during treatment were significantly associated with better disease control (odds ratio 8568.4, P = 0.000036).
- Elevated EV miR-497-5p levels during therapy correlated with prolonged progression-free survival (PFS) (hazard ratio = 0.27, P <0.000061).
Conclusions:
- EV miRNAs let-7g-5p and miR-497-5p are identified as novel predictive biomarkers for MAPKi treatment benefit in metastatic CMM.
- Assessing EV miRNAs during and after treatment may offer insights into resistance mechanisms and improve patient management.
Related Concept Videos
01:22MicroRNAs
01:22MicroRNAs
02:57Targeted Cancer Therapies
There are several types of targeted therapies against...
02:27Predicting Molecular Geometry
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
01:42The Extracellular Matrix