Work

RSC4All as a machine learning nomogram to predict RSClin™ results in HR+/HER2- node-negative early breast cancer

Year: 2025

Type: article

Abstract: Background: RSClin™ combines clinicopathological (CP) and genomic data to refine predictions of distant recurrence (DR) risk and chemotherapy (CT) benefit in node-negative (N0) hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative early breast cancer (eBC).Available in the United States, RSClin™ is inaccessible in Europe due to regulatory constraints.We developed and validated RSC4All, a machine learning (ML) nomogram replicating RSClin™ predictions and evaluating clinical impact by comparing DR risk and CT benefit estimates across recurrence score (RS), RSClin™, and RSC4All. Materials and methods:We retrospectively identified 290 HR-positive/HER2-negative eBC with both 21-gene RS and RSClin™ estimation at three European centers (development cohort).ML models were trained on 70% and internally validated on 30% of this cohort to reproduce RSClin™ estimates for DR risk and CT benefit.External validation used an independent cohort of 513 patients.Performance was assessed using receiver operating characteristic area under the curve (ROC AUC) (classification) and R 2 (regression).Clinical agreement between RSC4All and RSClin™ was evaluated using Cohen's kappa and McNemar's test, including replication of RS to RSClin™ reclassification patterns.RS and RSClin™ classifications were compared in the pooled population and CP predictors of reclassification were explored.Results: ML classification models achieved excellent performance with ROC AUC 0.99 for both DR risk and CT benefit, while regression model had R 2 = 0.82 and 0.77, respectively.In external validation, RSC4All maintained high accuracy (ROC AUC = 0.99; R 2 = 0.82 and 0.76) with an almost perfect clinical agreement with RSClin™ (Cohen's kappa = 0.87 and 0.84) and replicated RS to RSClin™ reclassification direction in 94.1% (DR) and 93.0%(CT) of cases.In the pooled cohort, RSClin™ reclassified 22.2% of DR risk (balanced up/downgrades) and 12.2% for CT benefit (mostly upgrades), with high grade, high Ki-67, low progesterone receptor, and larger tumor size predicting upward reclassification.A web-based tool was developed for public access (https://rsc4all.streamlit.app).Conclusions: RSC4All accurately reproduces RSClin™ predictions and reclassification patterns, providing a freely accessible alternative where RSClin™ is unavailable.By integrating CP and genomic data, it refines risk stratifications and supports adjuvant decisions in N0 HR-positive/HER2-negative eBC.External validation in an independent cohort strengthens its generalizability and clinical applicability.

Source: ESMO Open

DOI: 10.1016/j.esmoop.2025.105830