Prediction of BRAF V600E variant from cancer gene expression data

Introduction

BRAF gene encodes a serine/threonine kinase and is known to be an oncogene (1,2). BRAF regulates the mitogen-activated protein kinase (MAPK) pathway. The V600E is the most common somatic BRAF variant followed by V600K/D/R/M and non-V600 variants (3). Knowing the presence of these BRAF variants is important to make a plan for patient treatment, especially in melanoma and colorectal carcinoma.

The presence of BRAF variants is a marker to screen Lynch syndrome in microsatellite-unstable (MSI-H) colorectal cancer (4). Lynch syndrome is an autosomal dominant hereditary cancer syndrome associated with mismatch repair gene deficiency. The presence of a BRAF V600E variant suggests that MSI-H colorectal cancer is sporadic tumor rather than a component of Lynch syndrome-associated malignancy (5).

Real-time polymerase chain reaction (PCR) or next-generation sequencing were traditionally used for BRAF variant detection to select who will respond to the BRAF inhibitors. Recently immunohistochemistry and digital polymerase chain reaction are used for detecting BRAF V600E variant (6,7). BRAF inhibitors have been approved for the treatment of melanoma (8-10), non-small cell lung cancer (11), and colon cancer (12). The prediction of BRAF variants using gene expression data might be an alternative test when the direct variant sequencing test is not available or fails.

We have built prediction models to detect PIK3CA variants and homologous recombination deficiency with mRNA gene expression data using The Cancer Genome Atlas (TCGA) pan-cancer data (13). TCGA is a large cancer genomic consortium including more than 10,000 specimens from 25 different tumor types with exome sequencing, mRNA gene expression, DNA methylation, and clinical data (14). In this study, we try to develop a prediction model to detect BRAF V600E variant with mRNA gene expression data in various cancer types. We present the following article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-883/rc).

Methods

Dataset

We used TCGA pan-cancer data. The mRNA gene expression data were downloaded from the National Cancer Institute (NCI)’s Genomic Data Commons (GDC) website (https://gdc.cancer.gov/about-data/publications/pancanatlas). Data of BRAF variants were obtained from the cbioportal website (15).

We only included the presence of BRAF V600E variants as the target variable because BRAF inhibitors have been approved for cancers with BRAF V600E variants but not for other BRAF variants. Predictor variables were mRNA gene expression and cancer types. The mRNA gene expression predictor variables were filtered with a median absolute deviation to exclude less informative variables.

The BRAF V600E variants were frequently observed in thyroid carcinoma, cutaneous melanoma, and colon adenocarcinoma and very rarely observed in other cancer types. We used three-quarters of the three cancer types with a high prevalence of BRAF V600E variants for the training set and the remaining test set. The other cancer types with a low prevalence of BRAF V600E variants were regarded as an unseen test set.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Ethical approval is not required because we used public databases according to the TCGA publication guidelines (https://cancergenome.nih.gov/publications/guidelines).

Dataset summary

The number of included cases of the training set, the test set, and the unseen test set was 1,136, 376, and 9,377, respectively. A total of 5,129 mRNA gene expression predictors were selected after filtering with median absolute deviation. The prevalence of BRAF V600E variants was 0.57 (326/568 cases) for thyroid carcinoma, 0.33 (190/469 cases) for cutaneous melanoma, and 0.10 (49/475) for colon adenocarcinoma. Cancer type abbreviation of pan TCGA dataset and number of cases of each cancer type are summarized in Table S1.

Prediction modeling

We adopt a penalized logistic regression for the BRAF V600E variants prediction model (16). Tidymodels was used for the modeling process. Tidymodels is a framework that is a collection of R packages (R project for Statistical Computing, RRID:SCR_001905) for modeling and machine learning.

Penalized logistic regression has two hyperparameters which are the amount of regularization (λ) and the proportion of lasso penalty (α). Bootstrap resampling was used to determine those hyperparameters. Ten times bootstrap resampling was performed with a combined target variable and cancer type stratification.

Data preprocessing included knnimputation for missing value imputation, and YeoJohnson transformation for skewness correction, center, and scale for standardization, with the synthetic minority over-sampling technique (smote) for class imbalance.

Hyperparameter optimization with a grid search was done for model selection in terms of area under the precision-recall (AUPR). AUPR is better than area under the receiver operating characteristic (AUROC) to compare model performance with an imbalanced dataset (17). The hyperparameter grid was set into λ (10⁻⁵, 10⁻⁴, 10⁻³, 10⁻², 10⁻¹, 10⁰) and α (0.0, 0.25, 0.5, 0.75, 1.0).

Assessing model performance

Model performance was estimated on the test set of the cancer types with a high prevalence of BRAF V600E variants and the test set of other cancer types with a low prevalence of BRAF V600E variants as an unseen test set in terms of AUPR.

Gene ontology test

The gene ontology test was done with the PANTHER overrepresentation test (18) to determine which pathways are important in predicting the BRAF V600E variants. The selected predictor genes after final model fitting with entire training set were evaluated for gene ontology test with following detailed PANTHER parameters (analysis type: PANTHER Overrepresentation Test (Released 20210224), Annotation Version and Release Date: PANTHER version 16.0 Released 2020-12-01, Reference List: Homo sapiens (all genes in database), Test Type: FISHER, Correction: FDR).

Statistical analysis

All statistical analysis was done using R (R Project for Statistical Computing, RRID:SCR_001905).

Results

Model summary

The hyperparameter was chosen as 10⁻⁵ for λ and 0.25 for α. Those hyperparameter values showed the highest AUPR by 10 times bootstrap resampling. After model fitting with the entire training set and selected hyperparameters, 546 predictors were included in the final model. The cancer types were excluded from the final model. The coefficient values of genes that were included in the final model are summarized in Table S2. A predicted probability was calculated by the final logistic model after pre-determined data preprocessing. Genes with the largest positive coefficient value included ETS variant transcription factor 1 (ETV1), AKT serine/threonine kinase 2 (AKT2), neurofibromin 1 (NF1) and nuclear factor kappa B subunit 1 (NFKB1).

Performance of prediction model

The AUROC of BRAF V600E variant prediction on the training set was 0.99 in thyroid carcinoma, and 1.00 in colon adenocarcinoma and cutaneous melanoma. The AUROC on the test set was 0.98 in thyroid carcinoma, 0.90 in colon adenocarcinoma, and 0.85 in cutaneous melanoma. The receiver operating characteristic curve (ROC curve) is illustrated in Figure 1.

Figure 1 ROC curve of BRAF V600E variant prediction. THCA, thyroid carcinoma; SKCM, Cutaneous Melanoma; COAD, Colon adenocarcinoma; ROC, receiver operating characteristic.

The AUPR of BRAF V600 variant prediction on the training set was 0.99 in thyroid carcinoma, 1.00 in colon adenocarcinoma, and cutaneous melanoma. The AUPR on the test set was 0.98 in thyroid carcinoma, 0.71 in colon adenocarcinoma, and 0.65 in cutaneous melanoma. The precision-recall curve (PR curve) was illustrated in Figure 2.

Figure 2 The precision-recall curve of BRAF V600E variant prediction. PR, precision-recall.

AUROC was 0.52 and AUPR was 0.002 with 0.002 baselines on an unseen test set of other cancer types with a low prevalence of BRAF V600E variants.

Gene ontology test

The selected predictor genes were overrepresented in the following pathways: Insulin/IGF pathway-protein kinase B signaling cascade, PI3 kinase pathway, Endothelin signaling pathway, Integrin signaling pathway, Apoptosis signaling pathway, T cell activation, CCKR signaling map, Inflammation mediated by chemokine and cytokine signaling pathway, Gonadotropin-releasing hormone receptor pathway. Detailed gene ontology results are described in the Table S3.

Discussion

Our BRAF V600 variant prediction model showed very good performance on the test set of the cancer types including thyroid carcinoma, colon adenocarcinoma, and cutaneous melanoma. Those cancer types have a high prevalence of BRAF V600E variants. This result suggests that a BRAF V600 variant prediction model can help to select patients for treatment with BRAF inhibitors.

Gene expression signature has been used as a predictive biomarker in the practice of patient selection. Gene expression signature assay is recommended to select breast cancer patients who will benefit from receiving chemotherapy (19). These gene signature assay allow many breast cancer patients avoid adjuvant chemotherapy.

Although the purpose of this study is to investigate the possibility of BRAF V600E variants predictive model with mRNA gene expression data, we found that our model is biologically relevant because some genes that are biologically related to BRAF V600E variants had larger coefficient values. ETV1 is the predictor with the largest positive coefficient value. ETV1 is a member of the E twenty-six (ETS) family of transcription factors. ETS family genes make translocations with the ewing sarcoma breakpoint region 1 (EWSR1) gene in Ewing’s sarcoma/peripheral neuroectodermal tumor (PNET) spectrum and prostate cancer (20,21). The BRAF V600E variant is associated with ETV1 expression and brain metastasis in melanoma (22). ETS factors including ETV1 are upregulated in papillary thyroid cancer with the BRAF V600E variant and showed synergistic effect with TERT promoter mutation (23). Nuclear factor κB (NF-κB) is activated by BRAF V600E variant and promotes invasiveness in thyroid cancer (24,25). The BRAF V600E variant induces NF-κB activation and increases melanoma cell survival in melanoma (26). Genes in the RAF-MEK-ERK signal transduction pathway, including AKT serine/threonine kinase 2 (AKT2) and NF1, also showed larger coefficient values.

A previous study predicts BRAF variants using Affymetrix mRNA gene expression data with a support vector machine model from a panel of 63 melanoma cell lines with 0.794 ROCAUC (27). BRAF prediction studies using image data have been published. Ultrasound images with radiomics data were used for BRAF variant prediction with 0.651 ROCAUC (28). A deep learning model from the histologic image was also used for BRAF variant prediction in melanoma with 0.83 ROCAUC (29).

Our prediction model has some limitations. Our model showed poor performance on the test set of other cancer types with a low prevalence of BRAF V600E variants. BRAF inhibitors have been approved in patients with lung non-small cell carcinoma and BRAF V600E variants. The lung non-small cell carcinoma shows a low prevalence of BRAF V600E variants. Therefore, our prediction model cannot be applied to lung non-small cell carcinoma patients or other cancer types with a low prevalence of BRAF V600E variants. Gene expression data are expensive and still complex for clinical use.

In conclusion, our penalized logistic regression model can predict BRAF V600E variant with good performance in thyroid carcinoma, cutaneous melanoma and colon adenocarcinoma.

Acknowledgments

Funding: This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2021R1I1A1A01043754).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-883/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-883/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-883/coif). The Catholic University of Korea, Industry-Academic Cooperation Foundation has been filed a patent for “Modeling method for BRAF variant prediction model” (Application No. 10-2022-0014717). All authors are listed as inventors of the patent.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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