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. 2024 Jan 19;16(2):445.
doi: 10.3390/cancers16020445.

The Clinical, Genomic, and Transcriptomic Landscape of BRAF Mutant Cancers

Suzanne Kazandjian  1   2 , Emmanuelle Rousselle  3   4 , Matthew Dankner  3   4   5   6 , David W Cescon  7 , Anna Spreafico  7 , Kim Ma  1   2 , Petr Kavan  1   2 , Gerald Batist  1   2 , April A N Rose  1   2   3   4   5
Affiliations

The Clinical, Genomic, and Transcriptomic Landscape of BRAF Mutant Cancers

Suzanne Kazandjian et al. Cancers (Basel). .

Abstract

Background: BRAF mutations are classified into four molecularly distinct groups, and Class 1 (V600) mutant tumors are treated with targeted therapies. Effective treatment has not been established for Class 2/3 or BRAF Fusions. We investigated whether BRAF mutation class differed according to clinical, genomic, and transcriptomic variables in cancer patients.

Methods: Using the AACR GENIE (v.12) cancer database, the distribution of BRAF mutation class in adult cancer patients was analyzed according to sex, age, primary race, and tumor type. Genomic alteration data and transcriptomic analysis was performed using The Cancer Genome Atlas.

Results: BRAF mutations were identified in 9515 (6.2%) samples among 153,834, with melanoma (31%), CRC (20.7%), and NSCLC (13.9%) being the most frequent cancer types. Class 1 harbored co-mutations outside of the MAPK pathway (TERT, RFN43) vs. Class 2/3 mutations (RAS, NF1). Across all tumor types, Class 2/3 were enriched for alterations in genes involved in UV response and WNT/β-catenin. Pathway analysis revealed enrichment of WNT/β-catenin and Hedgehog signaling in non-V600 mutated CRC. Males had a higher proportion of Class 3 mutations vs. females (17.4% vs. 12.3% q = 0.003). Non-V600 mutations were generally more common in older patients (aged 60+) vs. younger (38% vs. 15% p < 0.0001), except in CRC (15% vs. 30% q = 0.0001). Black race was associated with non-V600 BRAF alterations (OR: 1.58; p < 0.0001).

Conclusions: Class 2/3 BRAFs are more present in Black male patients with co-mutations outside of the MAPK pathway, likely requiring additional oncogenic input for tumorigenesis. Improving access to NGS and trial enrollment will help the development of targeted therapies for non-V600 BRAF mutations.

Keywords: BRAF mutation; MAPK pathway; colorectal cancer; melanoma; non-small cell lung cancer.

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Conflict of interest statement

The following authors declare the listed conflicts of interest: David W. Cescon —Consultancy advisory relationships with AstraZeneca, Daiichi Sankyo, Eisai, Gilead, GlaxoSmithKline, Inflex, Inivata/NeoGenomics, Lilly, Merck, Novartis, Pfizer, Roche and Saga; research funding to their institution from AstraZeneca, Guardant Health, Gilead, GlaxoSmithKline, Inivata/NeoGenomics, Knight, Merck, Pfizer, ProteinQure and Roche; and holds a patent (US62/675,228) for methods of treating cancers characterized by a high expression level of spindle and kinetochore associated complex subunit 3 (ska3) gene. Anna Spreafico—Honoraria: Bristol Myers Squibb, Medison & Immunocore. Consulting or Advisory Role: Novartis, Merck, Bristol Myers Squibb, Oncorus, Medison & Immunocore. Research Funding: Bristol Myers Squibb, Novartis, Merck, Symphogen, AstraZeneca/MedImmune, Bayer, Surface Oncology, Janssen Oncology, Northern Biologics, Replimune, Roche, Alkermes, Array BioPharma, GlaxoSmithKline, Treadwell Therapeutics (Inst), Amgen (Inst). Travel, Accommodations, Expenses: Merck, Bristol Myers Squibb, Idera, Bayer, Janssen Oncology, Roche. Gerald Batist — Partner in a Genome Canada proteomics grant with AstraZeneca. April A.N. Rose—Employment: Merck. Recipient: an immediate family member Stock and Other Ownership interests: Merck by an immediate family member. Consulting or Advisory Role: EMD Serono, Advanced Accelerator Applications/Novartis Research Funding: Canadian Institutes of Health Research (CIHR), Canadian Cancer Society, Conquer Cancer Foundation, Jewish General Hospital Foundation, TransMedTech Institute, Canada Foundation for Innovation, AstraZeneca Canada, Merck, Pfizer, Seattle Genetics. The remaining authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The genomic landscape of BRAF mutant tumors. (A) Oncoprint highlighting the top 30 most frequent genes that are differentially altered between tumors with Class 1/2/3 BRAF mutations. (B) Histogram highlighting the incidence of gene alterations within each BRAF class. (C) The filtered list of genes that were significantly differentially altered according to BRAF Class 1/2 and 1/3 status across all cancers (n = 18 and n = 59, respectively, Q < 0.05) was subjected to pathway analysis using the MSigDB Hallmark algorithm. Pathways that were over-represented in this list of genes are indicated in blue, red, and green (p < 0.05 and Q < 0.05, p < 0.05, Q < 0.2, and p < 0.2 and Q < 0.2, respectively).
Figure 2
Figure 2
Gene alterations enriched in primary vs. metastatic BRAF mutant tumors. Gene alterations present in melanoma (A–C), CRC (D–F), and NSCLC (G–I) based on percent of samples with the gene alteration in primary (y-axis) or metastatic (x-axis) tumors. Genes that were present in a minimum of 1 primary and metastatic tumor are included. Pearson Correlation (R) was calculated for each panel. Colored dots represent significant enrichment (p < 0.05) in primary (blue) or metastatic (red) tumors (p value derived from two-sided Fisher Exact test).
Figure 3
Figure 3
The transcriptomic landscape of BRAF mutant tumors. (A) Heatmap of the differentially expressed genes (n = 93) in Class 1 (V600) vs. Class 2/3/Fusion (non-V600) BRAF mutant melanoma tumors (TCGA, n = 195 samples). (B) Top 10 GSEA gene sets enriched in non-V600 BRAF mutant melanoma tumors. (C) Heatmap of the differentially expressed genes (n = 182) in BRAF mutant V600 vs. non-V600 colorectal tumors (TCGA, n = 53 samples). (D) Top 10 GSEA gene sets enriched in non-V600 BRAF mutant colorectal tumors. (E) Heatmap of the differentially expressed genes (n = 160) in V600 vs. non-V600 BRAF mutant NSCLC tumors (TCGA, n = 32 samples). (F) Top 10 GSEA gene sets enriched in non-V600 BRAF mutant NSCLC tumors. Genes plotted for each heatmap can be found in Supplementary Tables S19–S21.
Figure 4
Figure 4
Distribution of BRAF mutation class according to sex, age, and race across cancer type. The frequency of each BRAF class is shown in subgroups defined by sex (A), age (B), and primary race (C) among all patients, melanoma, CRC, NSCLC, and all other cancers (all patients excluding melanoma, CRC, and NSCLC). Values shown within each category represent the proportion of patients expressing each BRAF Class within cancer types according to sex (A), age (B), and primary race (C). p-value was calculated through the chi-square test for each contingency table and was then corrected using the Benjamini–Hochberg method to determine false discovery rate–corrected q value, which was considered significant when q was less than 0.05.
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