Abstract 68P
Background
Gene expression profiles (GEP) are commonly employed to aid decision making for adjuvant chemotherapy administration in ER+/HER2- breast cancer (luminal BC). We aimed to improve upon available tools by deploying a machine learning model which leverages gene expression and clinical data.
Methods
Using the R package MetaGxBreast, we selected patients with early-stage luminal BC that had not received chemotherapy. Four commercially available GEP (70-gene signature, PAM50 risk of recurrence score, 21-gene recurrence score/RS, and 12-gene score), the 14-gene immunoglobulin (IGG) signature, and a clinical composite risk score (CCRS), summarizing age, nodal status, tumour size, and grade, were calculated for each patient. Multivariable Cox regression evaluated their independent association with distant metastasis-free survival (DMFS). Using the most informative variables, we developed a random survival forest (RSF) model, which is a nonparametric ensemble learner for survival data. An optimal cutoff for model’s risk predictions was determined and evaluated on a pooled external validation set of three independent in-house cohorts.
Results
The training set comprises 1694 luminal BC patients. Each of the five signatures was independently prognostic after adjustment for clinical variables. Multivariable Cox regression showed independent prognostic associations of the 21-gene RS (HR 2.84, 95% CI 1.79–4.5, p<0.001), the 14-gene IGG signature (HR 10.3, 95% CI 4.35–24.4, p<0.001), and the CCRS (HR 5.76, 95% CI 3.6–9.2, p<0.001). Using these scores as features, an RSF model was built and achieved a C-index of 0.95, and an integrated cumulative/dynamic AUC of 0.83. Applying the optimal cutoff on the external validation set (n=269), we identified a population of 40.5% of the cohort with 15-year DMFS of 90.9%. By comparison, RS low risk (RS<11) isolated 14.4% of the cohort with 91.7% 15-year DMFS.
Conclusions
We developed and validated a clinically feasible machine learning model for early-stage luminal BC, integrating gene expression and clinical data, that identified a larger ultra-low risk population than currently achieved by commercial tools. Further studies are needed to validate its analytical accuracy and clinical utility.
Legal entity responsible for the study
Karolinska Institutet.
Funding
Has not received any funding.
Disclosure
J. Bergh: Other, Fees (honoraria) to Coronis and Asklepios Cancer Research AB as an invited speaker/chair from AstraZeneca and Roche, respectively: Coronis and Asklepios Cancer Research AB.; Other, Institutional honoraria as chapter co-author for UpToDate to Asklepios Medicin HB. Co-author on a chapter on ”Prognostic and Predictive factors in early, non-metastatic breast cancer”: Asklepios Medicin; Other, Institutional research grants received more than ten years ago to Karolinska Institutet and/or University Hospital for molecular marker studies/ clinical studies (we are still working with the material). No personal payments for these activities: Amgen, AstraZeneca, Bayer, Merck, Pfizer, Roche and Sanofi -Aventis; Other, Stocks in Stratipath AB. The company is involved in AI based diagnostics for breast cancer: Stratipath AB. T. Foukakis: Financial Interests, Institutional, Invited Speaker: Roche, AstraZeneca, Gilead Sciences; Financial Interests, Personal, Royalties, Authorship of two chapters in UpToDate: Wolters Kluwer; Financial Interests, Institutional, Invited Speaker, Clinical trial support (research grant and study drug): AstraZeneca; Financial Interests, Institutional, Invited Speaker, Clinical trial support (research grant and study drug): Novartis; Financial Interests, Institutional, Invited Speaker, Discount on the Prosigna PAM50 assay in ARIADNE clinical trial: Veracyte. A. Matikas: Financial Interests, Institutional, Invited Speaker: Seagen; Financial Interests, Institutional, Invited Speaker, International co-PI of academic trial ARIADNE (EU CT: 2022-501504-95-00): AstraZeneca, Novartis, Veracyte; Financial Interests, Institutional, Invited Speaker, Registry study: Merck; Non-Financial Interests, Advisory Role: Veracyte, Roche. All other authors have declared no conflicts of interest.