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Updated: Aug 17, 2025
Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines
Published on: October 8, 2015
Prediction of BRAF V600E variant from cancer gene expression data
Jun Kang1, Jieun Lee2, Ahwon Lee1,3
1Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Background:
BRAF inhibitors have been approved for the treatment of melanoma, non-small cell lung cancer, and colon cancer. Real-time polymerase chain reaction or next-generation sequencing were clinically used for BRAF variant detection to select who responds to BRAF inhibitors. The prediction of BRAF variants using gene expression data might be an alternative test when the direct variant sequencing test is not feasible. In this study, we built a prediction model to detect BRAF V600 variants with mRNA gene expression data in various cancer types.
Methods:
We adopted a penalized logistic regression for the BRAF V600E variants prediction model. Ten times bootstrap resampling was done with a combined target variable and cancer type stratification. Data preprocessing included knnimputation for missing value imputation, YeoJohnson transformation for skewness correction, center, and scale for standardization, synthetic minority over-sampling technique for class imbalance. Hyperparameter optimization with a grid search was undertaken for model selection in terms of area under the precision-recall.
Results:
The area under the curve of the receiver operating characteristic curve on the test set was 0.98 in thyroid carcinoma, 0.90 in colon adenocarcinoma, and 0.85 in cutaneous melanoma. The area under the precision-recall of the test set was 0.98 in thyroid carcinoma, 0.71 in colon adenocarcinoma, and 0.65 in cutaneous melanoma.
Conclusions:
Our penalized logistic regression model can predict BRAF V600E variants with good performance in thyroid carcinoma, cutaneous melanoma, and colon adenocarcinoma.
Insights
This study developed a penalized logistic regression model to predict BRAF V600E variants using gene expression data, offering a viable alternative to direct sequencing for guiding BRAF inhibitor treatment in various cancers.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- BRAF inhibitors are approved for melanoma, lung, and colon cancers.
- Current BRAF variant detection uses PCR or NGS.
- Predicting BRAF variants from gene expression offers an alternative when direct sequencing is not feasible.
Purpose of the Study:
- To build a prediction model for BRAF V600 variants using mRNA gene expression data.
- To evaluate the model's performance across different cancer types.
Main Methods:
- Penalized logistic regression model was employed.
- Data preprocessing included imputation, transformation, standardization, and over-sampling.
- Hyperparameter optimization was performed using grid search for model selection.
Main Results:
- The model achieved an AUC of 0.98 for thyroid carcinoma, 0.90 for colon adenocarcinoma, and 0.85 for cutaneous melanoma.
- The area under the precision-recall curve was 0.98 for thyroid carcinoma, 0.71 for colon adenocarcinoma, and 0.65 for cutaneous melanoma.
Conclusions:
- The developed penalized logistic regression model demonstrates good performance in predicting BRAF V600E variants.
- This predictive approach is effective in thyroid carcinoma, cutaneous melanoma, and colon adenocarcinoma.
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