Predicting BRAF V600E variants: yet another new method

BRAF V600 variants, especially BRAF V600E, are known to be important driver mutations in melanoma (1,2), non-small cell lung cancer (3), and colon cancer (4), as BRAF inhibitors ± MEK inhibitors have been approved for treatment in these cancers. Thus, finding BRAF V600 variants in these cancers is mandatory to provide optimal treatment. In the perfect world, next generation sequencing (NGS) of all cancers would be possible at diagnosis. However, this is not always the case, so robust, alternative methods of detection are welcomed.

In the present issue of Translational Cancer Research, Kang et al. build a prediction model to detect BRAF V600 variants with mRNA gene expression data in various cancer types. The authors obtained mRNA gene expression data of BRAF V600-variant cancers from The Cancer Genome Atlas (TCGA) pan-cancer database, and constructed a training set from thyroid carcinoma, cutaneous melanoma and colon adenocarcinoma cases, which are known to have high prevalence of BRAF V600 alterations. The authors then adopted a penalized logistic regression for prediction of BRAF V600E variants. Area under the receiver operating characteristic (AUROC) and area under the precision-recall (AUPR) for the test set was 0.98 and 0.98 in thyroid carcinoma, 0.90 and 0.71 in colon carcinoma, and 0.85 and 0.65 in cutaneous melanoma, respectively. However, AUROC and AUPR was low in the unseen test set for cancer types with low prevalence of BRAF V600 variants. These results suggested that this prediction model can reliably detect BRAF V600E-variant cases using mRNA expression data in cancer types with high incidence of BRAF V600E, but not those with low incidence.

Limitations of this method include poor performance of the prediction model on cancer types with low prevalence of BRAF V600E, including non-small cell lung carcinoma and inability to reliably predict non-BRAF V600E, BRAF variants. Additionally, obtaining gene expression data is still expensive and complex for clinical use.

Easily accessible methods of detecting BRAF V600E include immunohistochemistry (IHC) (5-7) and Sanger sequencing (6,8). However, false-positive staining can be observed by IHC and false-negative results by Sanger sequencing can be obtained when tumor DNA volume is low (6). More sensitive methods such as allele specific quantitative PCR (ASQ-PCR) (6), digital PCR (dPCR) (9,10), high-resolution melting analysis (11) and IntelliPlex^TM multiplex system (12) exist, but they are less clinically available. Finally, several NGS cancer genome panels exist, most able to detect BRAF alterations, although not universally accessible due to high cost, approval for analysis at relapse but not initial diagnosis, and the process of clinical laboratory improvement amendments (CLIA) certification (13).

Incidence of BRAF V600 variant is highly variable depending on tumor type. Incidence is high in cutaneous melanoma (60%), colorectal cancer (12%) and some brain tumors including papillary craniopharyngioma (95%), epithelioid glioblastoma (50%), pleomorphic xanthoastrocytoma (70%) and ganglioglioma (50%) (14). For these tumors, detection of BRAF V600 variants should be considered. However, for those tumor types with lower incidences, assessment of BRAF variants in all tumors is not standard. In these tumor types, understanding clinical, radiographical, or pathological characteristics that point toward a possible alteration in BRAF V600 is vital. In colorectal cancer, detection of microsatellite instability-high (MSI-H) suggests BRAF V600 alteration, especially in non-Lynch syndrome patients (15). For glioblastoma, which has a low prevalence of BRAF V600-variant at 3%, several studies have suggested that radiographic characteristics including cortical involvement, presence of cystic component, well circumscribed lesions and hemorrhagic onset are suggestive of the BRAF V600E-variant (16-18). Female preponderance, unfavorable outcome, presence of micropapillary architecture, prominent lepidic (bronchioalveolar-like) growth, and focal clear cell changes point to BRAF alterations in non-small cell lung cancer (5,11,19,20).

Finally, Kang et al. (21) provide interesting gene ontology data suggesting that selected genes for prediction of BRAF V600E-variant are 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, and gonadotropin-releasing hormone receptor pathway, providing insight into the unique biology of BRAF V600E-variant tumors.

In conclusion, BRAF V600E-variant cancers are candidates for molecularly targeted therapy. Prediction of this variant is very important, although not always easy. Kang et al. provide a new prediction model based on cancer mRNA expression data with potential clinical implications.

Acknowledgments

Funding: This work was funded by Japan Society for the Promotion of Science (JSPS) KAKENHI grant (No. 21KK0156).

Footnote

Provenance and Peer review: This article was commissioned by the editorial office of Translational Cancer Research. The article did not undergo external peer review.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-2384/coif). MN declares speaker honoraria from Chugai, Daiichi Sankyo, Eisai and Novocure, all outside of the submitted work. The author has no other conflicts of interest to declare.

Ethical Statement: The author is 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 investigate and resolved.

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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