Development and Validation of the PREMMplus Model for Multigene Hereditary Cancer Risk Assessment

Abstract

Purpose: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

FIG 1.

NB curves of the PREMMplus score for identifying individuals with category A gene PGVs in (A) the development cohort, (B) the clinic-based validation cohort, and (C) the laboratory-based validation cohort. The result for the PREMMplus score is the teal dashed line, and the two other lines are for testing all probands (solid red line) and testing no probands (solid blue line). NB is defined as [true-positive rate – (false-positive rate × weighting factor)], where the weighting factor is given by threshold probability/(1 – threshold probability). Threshold probability refers to the point where people consider that the benefit of taking a panel test for identifying PGV(s) is equivalent to the harm of taking an unnecessary panel test. Threshold probability reflects how people weight the benefits and harms associated with the decision. When the threshold probability is 2.5%, the weighting factor is 1/39, which reflects that having one true positive is equivalent to not having 39 false positives in terms of benefit. For those who consider that having one true positive is equivalent to not having 19 false positives in terms of benefit, the corresponding threshold probability is 5.0%. Using the PREMMplus score offers a larger NB, compared with testing all probands and testing no probands, for those who have such a benefit/harm balance that corresponds to a 2.5% or higher threshold probability. NB, net benefit; PGV, pathogenic germline variant.

FIG A1.

Calibration plots of the PREMMplus score for identifying individuals with category A gene PGVs in (A) the development cohort, (B) the clinic-based validation cohort, and (C) the laboratory-based validation cohort. We divided each cohort into 20 ventiles, on the basis of PREMMplus scores. For each ventile, we calculated the average PREMMplus score (expected) and the proportion of probands carrying a PGV in any of the category A genes (observed). PGV, pathogenic germline variant.