Bellomo Domenico, Arias-Mejias Suzette M, Ramana Chandru, Heim Joel B, Quattrocchi Enrica, Sominidi-Damodaran Sindhuja, Bridges Alina G, Lehman Julia S, Hieken Tina J, Jakub James W, Pittelkow Mark R, DiCaudo David J, Pockaj Barbara A, Sluzevich Jason C, Cappel Mark A, Bagaria Sanjay P, Perniciaro Charles, Tjien-Fooh Félicia J, van Vliet Martin H, Dwarkasing Jvalini, Meves Alexander
SkylineDx B.V., Rotterdam, NL.
Mayo Clinic, Rochester, MN, USA.
JCO Precis Oncol. 2020;4:319-334. doi: 10.1200/po.19.00206. Epub 2020 Apr 14.
More than 80% of patients who undergo sentinel lymph node (SLN) biopsy have no nodal metastasis. Here we describe a model that combines clinicopathologic and molecular variables to identify patients with thin and intermediate thickness melanomas who may forgo the SLN biopsy procedure due to their low risk of nodal metastasis.
Genes with functional roles in melanoma metastasis were discovered by analysis of next generation sequencing data and case control studies. We then used PCR to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 754 consecutive thin and intermediate thickness primary cutaneous melanomas. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. A penalized maximum likelihood estimation algorithm was used to train logistic regression models in a repeated cross validation scheme to predict the presence of SLN metastasis from molecular, clinical and histologic variables.
Expression of genes with roles in epithelial-to-mesenchymal transition (glia derived nexin, growth differentiation factor 15, integrin β3, interleukin 8, lysyl oxidase homolog 4, TGFβ receptor type 1 and tissue-type plasminogen activator) and melanosome function (melanoma antigen recognized by T cells 1) were associated with SLN metastasis. The predictive ability of a model that only considered clinicopathologic or gene expression variables was outperformed by a model which included molecular variables in combination with the clinicopathologic predictors Breslow thickness and patient age; AUC, 0.82; 95% CI, 0.78-0.86; SLN biopsy reduction rate of 42% at a negative predictive value of 96%.
A combined model including clinicopathologic and gene expression variables improved the identification of melanoma patients who may forgo the SLN biopsy procedure due to their low risk of nodal metastasis.
接受前哨淋巴结(SLN)活检的患者中,超过80%没有淋巴结转移。在此,我们描述一种模型,该模型结合临床病理和分子变量,以识别薄型和中间厚度黑色素瘤患者,这些患者可能因淋巴结转移风险低而无需进行SLN活检程序。
通过分析下一代测序数据和病例对照研究,发现了在黑色素瘤转移中起作用的基因。然后,我们使用聚合酶链反应(PCR)对754例连续的薄型和中间厚度原发性皮肤黑色素瘤的前瞻性设计存档队列中的诊断性活检组织中的基因表达进行定量。感兴趣的结果是黑色素瘤诊断后90天内SLN活检转移情况。使用惩罚最大似然估计算法在重复交叉验证方案中训练逻辑回归模型,以根据分子、临床和组织学变量预测SLN转移的存在。
在上皮-间质转化(神经胶质衍生的凝血酶调节蛋白、生长分化因子15、整合素β3、白细胞介素8、赖氨酰氧化酶同源物4、转化生长因子β受体1型和组织型纤溶酶原激活剂)和黑素体功能(T细胞识别的黑色素瘤抗原1)中起作用的基因表达与SLN转移相关。仅考虑临床病理或基因表达变量的模型的预测能力不如包含分子变量与临床病理预测指标Breslow厚度和患者年龄相结合的模型;曲线下面积(AUC)为0.82;95%置信区间(CI)为0.78 - 0.86;在阴性预测值为96%时,SLN活检减少率为42%。
包括临床病理和基因表达变量的联合模型改善了对因淋巴结转移风险低而可能无需进行SLN活检程序的黑色素瘤患者的识别。