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PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗
PubMed2026

primary analysis of the RANDOMIZED eortc-2139/columbus-ad trial: Adjuvant encorafenib and binimetinib versus placebo in high-risk stage II BRAF-V600E/K melanoma.

PURPOSE: Stage IIB/IIC melanoma has a high risk of recurrence after resection. Combined BRAF/MEK inhibitor therapy showed benefit in resected high-risk stage III and advanced melanoma. The objective of this study was to investigate its role in stage IIB/IIC. METHODS: Adult patients with resected stage IIB/IIC cutaneous melanoma which had a BRAF V600E/K mutation were randomized 1:1 to receive encorafenib (enco) 450 mg QD + binimetinib (bini) 45 mg BID orally for one year or placebo. The study planned to randomize 815 patients and was designed to demonstrate superiority regarding recurrence-free survival (RFS). Following a premature termination of accrual, the study was amended with safety as the primary endpoint and RFS as secondary endpoint. RESULTS: Between June 9, 2022, and October 9, 2023, 339 patients were screened for a BRAF mutation and 110 randomized. Data cutoff was 19 Nov. 2024, after the last patient discontinued study participation. Among randomized patients, 87 (79%) had a BRAF V600E mutation, and 39 (35%) AJCC8 stage IIC. Median follow-up was 12 and 7 months for enco/bini and placebo arms, respectively. Among 54 patients who initiated enco + bini, grade ≥ 3 treatment-related adverse events (AE) occurred in 13 (24%) patients, and 18 (33%) patients had an AE leading to permanent treatment discontinuation. RFS at 12 months was 86% (95% CI: 65-95%) in the enco + bini and 70% (95% CI: 46-85%) in the placebo arm, distant metastasis-free survival at 12 months was 92% (95% CI: 77-97%) for enco + bini and 82% (95% CI: 55-93%) for placebo. CONCLUSION: EORTC 2139 - Columbus-AD demonstrated a consistent and manageable safety profile and encouraging efficacy results for the combination of enco and bini in resected stage IIB/C BRAF V600E/K-mutated cutaneous melanomas.

Adult↗
PubMed2026

COMBI-I: Long-Term Overall Survival With Spartalizumab Plus Dabrafenib and Trametinib in BRAF V600-Mutant Advanced Melanoma.

The COMBI-I trial (ClinicalTrials.gov identifier: NCT02967692) evaluating spartalizumab plus dabrafenib and trametinib (sparta-DabTram, n = 267) versus placebo plus dabrafenib and trametinib (placebo-DabTram, n = 265) for BRAF V600-mutant unresectable or metastatic melanoma failed to reach its primary end point of progression-free survival at 24 months. This final analysis reports overall survival (OS) during at least 5 years of extended follow-up. At the end of the trial (August 21, 2024), the median duration of follow-up was 76.9 months (range, 73.7-83.3 months). The median OS was 61.5 months (95% CI, 41.6 to not evaluable) for the sparta-DabTram arm and 41.6 months (95% CI, 30.6 to 56.9) for the placebo-DabTram arm (hazard ratio, 0.760 [95% CI, 0.598 to 0.966]). The safety findings were consistent with the known safety profile for sparta-DabTram. The most common treatment-related adverse event (TRAE) was pyrexia (65.9% v 46.2%, respectively, in the two study arms). Grade ≥3 TRAEs were reported in 57.3% and 36.7% of patients in the two arms, respectively. The combination of sparta-DabTram appears to improve OS compared with dabrafenib and trametinib alone in patients with BRAF V600-mutant metastatic melanoma.

Adult↗
PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗