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 [APC, BRCA1/2, CDH1, EPCAM, MLH1, MSH2, MSH6, biallelic MUTYH, PMS2, and TP53] and eight category B genes [ATM, BRIP1, CDKN2A, CHEK2, PALB2, PTEN, RAD51C, and RAD51D]). 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.

随着用于遗传性癌症风险评估的多基因面板测试 (MGPT) 的可用性，临床医生需要同时评估多个基因中致病性种系变异 (PGV) 的可能性。本研究的目的是开发和验证用于 MGPT 风险评估的临床预测模型 (PREMMplus)。

PREMMplus 是在一个由 7,280 名接受过 MGPT 的个人组成的队列中开发的。使用具有最小绝对收缩率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、种族和 18 种癌症/肿瘤的个人/家族史），以估计一个人在 19 个基因中携带 PGV 的可能性（按表型广泛分类）重叠和/或相对外显率：11 类 A [ APC、BRCA1 / 2、CDH1、EPCAM、MLH1、MSH2、MSH6、双等位基因MUTYH、PMS2和TP53] 和八个 B 类基因 [ ATM、BRIP1、CDKN2A、CHEK2、PALB2、PTEN、RAD51C和RAD51D ]）。模型性能在 8,691 人和 14,849 人的非重叠数据集中进行了验证，这些人分别从临床和实验室环境中确定了先前的 MGPT。

PREMMplus（得分 ≥ 2.5%）在开发和验证队列中识别 A 类基因 PGV 携带者的敏感性分别为 93.9%、91.7% 和 89.3% 以及 98.3%、97.5% 和 97.8% 的阴性预测值 (NPV) . PREMMplus 评估（得分≥ 2.5%）对于识别 A/B 类基因 PGV 携带者的敏感性分别为 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% 的 PGV 概率的个人进行 MGPT。

PREMMplus 准确识别具有高灵敏度/NPV 的各种癌症易感基因中的 PGV 个体。PREMMplus 分数 ≥ 2.5% 的个人应考虑进行 MGPT。