8 research outputs found

    Dependence of the predicted probability of <i>BRAF</i> mutation according to the four most influential variables; main effects model.

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    <p>Individual variable effects for each compartment were standardized for the effects of the other three compartments as shown by the vertical dashed red lines. The main effects model allows estimation of the <i>BRAF</i> mutation probability for each combination of levels of the four variables: as indicated by the vertical dashed red lines, the estimated <i>BRAF</i> mutation probability for a 60-year-old patient at the Heidelberg Center with melanoma subtype SSM and UV exposure was 39.6% (95% confidence interval: 23.1–58.8). The vertical blue lines represent the 95%-confidence intervals, plotted for each level of categorical covariates. In the case of continuous variables, such as age, a 95% confidence interval was plotted (gray). ALM, acral lentiginous melanoma; LMM, lentigo maligna melanoma; NM, nodular melanoma; SSM, superficial spreading melanoma; UV, ultraviolet.</p

    Variables that influence <i>BRAF</i> mutation probability: A next-generation sequencing, non-interventional investigation of <i>BRAFV600</i> mutation status in melanoma

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    <div><p>Background</p><p>The incidence of melanoma, particularly in older patients, has steadily increased over the past few decades. Activating mutations of <i>BRAF</i>, the majority occurring in <i>BRAFV600</i>, are frequently detected in melanoma; however, the prognostic significance remains unclear. This study aimed to define the probability and distribution of <i>BRAFV600</i> mutations, and the clinico-pathological factors that may affect <i>BRAF</i> mutation status, in patients with advanced melanoma using next-generation sequencing.</p><p>Materials and methods</p><p>This was a non-interventional, retrospective study of <i>BRAF</i> mutation testing at two German centers, in Heidelberg and Tübingen. Archival tumor samples from patients with histologically confirmed melanoma (stage IIIB, IIIC, IV) were analyzed using PCR amplification and deep sequencing. Clinical, histological, and mutation data were collected. The statistical influence of patient- and tumor-related characteristics on <i>BRAFV600</i> mutation status was assessed using multiple logistic regression (MLR) and a prediction profiler.</p><p>Results</p><p><i>BRAFV600</i> mutation status was assessed in 453 samples. Mutations were detected in 57.6% of patients (n = 261), with 48.1% (n = 102) at the Heidelberg site and 66.0% (n = 159) at the Tübingen site. The decreasing influence of increasing age on mutation probability was quantified. A main effects MLR model identified age (p = 0.0001), center (p = 0.0004), and melanoma subtype (p = 0.014) as significantly influencing <i>BRAFV600</i> mutation probability; ultraviolet (UV) exposure showed a statistical trend (p = 0.1419). An interaction model of age versus other variables showed that center (p<0.0001) and melanoma subtype (p = 0.0038) significantly influenced <i>BRAF</i> mutation probability; age had a statistically significant effect only as part of an interaction with both UV exposure (p = 0.0110) and melanoma subtype (p = 0.0134).</p><p>Conclusions</p><p>This exploratory study highlights that testing center, melanoma subtype, and age in combination with UV exposure and melanoma subtype significantly influence <i>BRAFV600</i> mutation probability in patients with melanoma. Further validation of this model, in terms of reproducibility and broader relevance, is required.</p></div

    <i>BRAF</i> mutation probability changes as a function of age and UV exposure (yes/no).

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    <p>Melanoma subtype was defined as (A) unknown, (B) SSM, (C) NM, (D) LMM, and (E) ALM. Samples were collected from UV exposed area (yes; blue) or non-UV exposed areas (no; red). ALM, acral lentiginous melanoma; LMM, lentigo maligna melanoma; NM, nodular melanoma; SSM, superficial spreading melanoma.</p
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