Dana-Farber Cancer Institute, Boston, MA.
Harvard Medical School, Boston, MA.
J Clin Oncol. 2022 Dec 10;40(35):4083-4094. doi: 10.1200/JCO.22.00120. Epub 2022 Aug 12.
With the availability of multigene panel testing (MGPT) for hereditary cancer risk assessment, clinicians need to assess the likelihood of pathogenic germline variants (PGVs) across numerous genes in parallel. This study's aim was to develop and validate a clinical prediction model (PREMMplus) for MGPT risk assessment.
PREMMplus was developed in a single-institution cohort of 7,280 individuals who had undergone MGPT. Logistic regression models with Least Absolute Shrinkage and Selection Operator regularization were used to examine candidate predictors (age, sex, ethnicity, and personal/family history of 18 cancers/neoplasms) to estimate one's likelihood of carrying PGVs in 19 genes (broadly categorized by phenotypic overlap and/or relative penetrance: 11 category A [, /, , , , , , biallelic , , and ] and eight category B genes [, , , , , , , and ]). Model performance was validated in nonoverlapping data sets of 8,691 and 14,849 individuals with prior MGPT ascertained from clinic- and laboratory-based settings, respectively.
PREMMplus (score ≥ 2.5%) had 93.9%, 91.7%, and 89.3% sensitivity and 98.3%, 97.5%, and 97.8% negative-predictive value (NPV) for identifying category A gene PGV carriers in the development and validation cohorts, respectively. PREMMplus assessment (score ≥ 2.5%) had 89.9%, 85.6%, and 84.2% sensitivity and 95.0%, 93.5%, and 93.5% NPV, respectively, for identifying category A/B gene PGV carriers. Decision curve analyses support MGPT for individuals predicted to have ≥ 2.5% probability of a PGV.
PREMMplus accurately identifies individuals with PGVs in a diverse spectrum of cancer susceptibility genes with high sensitivity/NPV. Individuals with PREMMplus scores ≥ 2.5% should be considered for MGPT.
随着多基因panel 检测(MGPT)用于遗传性癌症风险评估,临床医生需要同时平行评估许多基因中的致病性种系变异(PGV)的可能性。本研究旨在开发和验证用于 MGPT 风险评估的临床预测模型(PREMMplus)。
PREMMplus 是在一家机构的 7280 名接受 MGPT 的个体的队列中开发的。使用最小绝对值收缩和选择算子正则化的逻辑回归模型来检查候选预测因子(年龄、性别、种族以及个人/家族 18 种癌症/肿瘤史),以估计个体携带 19 种基因中的 PGV 的可能性(根据表型重叠和/或相对外显率进行广泛分类:11 个 A 类[、/、、、、、、双等位基因、和]和 8 个 B 类基因[、、、、、、、和])。在来自诊所和实验室的具有先前 MGPT 确定的非重叠数据集中(分别为 8691 名和 14849 名个体)对模型性能进行了验证。
PREMMplus(评分≥2.5%)在发展和验证队列中分别对 A 类基因 PGV 携带者的识别具有 93.9%、91.7%和 89.3%的敏感性和 98.3%、97.5%和 97.8%的阴性预测值(NPV)。PREMMplus 评估(评分≥2.5%)在识别 A 类/B 类基因 PGV 携带者方面的敏感性分别为 89.9%、85.6%和 84.2%,NPV 分别为 95.0%、93.5%和 93.5%。决策曲线分析支持对预测有≥2.5%PGV 概率的个体进行 MGPT。
PREMMplus 可准确识别具有多种癌症易感性基因中 PGV 的个体,具有高敏感性/NPV。PREMMplus 评分≥2.5%的个体应考虑进行 MGPT。