Real World Evaluation of the Prosigna/PAM50 Test in a Node-Negative Postmenopausal Swedish Population: A Multicenter Study

Abstract

Molecular signatures to guide decisions for adjuvant chemotherapy are recommended in early ER-positive, HER2-negative breast cancer. The objective of this study was to assess what impact gene-expression-based risk testing has had following its recommendation by Swedish national guidelines. Postmenopausal women with ER-positive, HER2-negative and node negative breast cancer at intermediate clinical risk and eligible for chemotherapy were identified retrospectively from five Swedish hospitals. Tumor characteristics, results from Prosigna® test and final treatment decision were available for all patients. Treatment recommendations were compared with the last version of regional guidelines before the introduction of routine risk signature testing. Among the 360 included patients, 41% (n = 148) had a change in decision for adjuvant treatment based on Prosigna® test result. Out of the patients with clinical indication for adjuvant chemotherapy, 52% (n = 118) could avoid treatment based on results from Prosigna® test. On the contrary, 23% (n = 30) of the patients with no indication were escalated to receive adjuvant chemotherapy after testing. Ki67 could not distinguish between the Prosigna® risk groups or intrinsic subtypes and did not significantly differ between patients in which decision for adjuvant therapy was changed based on the test results. In conclusion, we report the first real-world data from implementation of gene-expression-based risk assessment in a Swedish context, which may facilitate the optimization of future versions of the national guidelines.

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

“…Differences in the prognostic performance between different assays can be explained by several factors, including different molecular markers included in the gene signature assays, where the Prosigna ROR score is largely determined by proliferative features whereas others are driven by ER-related features(47). We found a significant correlation between the proliferation marker Ki67 and ROR score (Spearman’s rho 0.596) in this clinical case series, which is in line with previous findings(24). A significant association between Prosigna risk categories and Ki67 status was observed in all patients, and in low- vs intermediate-risk groups and in low- vs high-risk groups in the grade 2 subgroup.…”

“…All tumours had therefore previously been analysed with the Prosigna assay in clinical routine at point of diagnosis, between the years 2020 and 2022, to evaluate their risk of recurrence according to the Swedish national guidelines 35 . The cohort has partly been expanded from Kjällquist et al 24 . The Prosigna assay had been performed at the Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, on sections from formalin-fixed paraffin-embedded (FFPE) breast cancer tissue blocks, according to the manufacturer’s instructions (Veracyte, South San Francisco, CA, USA) on the nCounter system (NanoString Technologies, Seattle, WA, USA) as part of clinical routine.…”

“…Approximately 50% of all (17)(18)(19)(20), and around 60% of ERpositive/HER2-negative (1,21) breast cancer are classified as histological grade 2, which is a heterogenous group of tumours with variations in terms of aggressiveness and prognosis (22,23), thus, associated with limited value to guide decisions on choice of therapy. A limitation in clinical decision-making is, despite the use of prognostic multigene assays, that tests such as Prosigna may classify up to 44% of histological grade 2 tumours as intermediate risk (24), which does not add any clinically actionable information. In addition, the diagnostic multigene assays for breast cancer risk stratification show discordances in risk categorisation between different tests (12,25).…”

Adherence to and optimization of guidelines for Risk of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

“…Therefore, improved pre-selection of patients needed to test is warranted, mirroring the algorithms designed for RS/Oncotype Dx testing. We have previously reported on the real-world impact of ROR/Prosigna testing on administration of adjuvant chemotherapy in a Swedish multicenter study [15]. In the present study, we report how patient selection for testing with GEP was affected by clinical risk factors, the adherence to regional/national guidelines, and describe how selection for testing can be optimized by employing machine-learning models.…”

Clinically relevant gene signatures provide independent prognostic information in older breast cancer patients

“…We sampled 100 subsets to compute the median of expression of the probes for centering the data (31). The gene signatures were chosen owing to their relevance in randomized clinical trials such as MINDACT (13), ASTER70 (22), TAILORX (32) and also in real world evaluation (33).…”