221P - A clinically applicable model to predict risk of relapse in patients treated for locally advanced breast cancer. Potential utilization in future c...

Date 11 September 2017
Event ESMO 2017 Congress
Session Poster display session
Topics Breast Cancer, Locally Advanced
Breast Cancer
Presenter Olexiy Aseyev
Citation Annals of Oncology (2017) 28 (suppl_5): v68-v73. 10.1093/annonc/mdx364
Authors O. Aseyev, L. Simmonds, M. Gertler, S. Verma
  • Medical Oncology, The Ottawa Hospital Regional Cancer Centre, K1H 8L6 - Ottawa/CA

Abstract

Background

Despite advances in cancer treatment, over 25% of patients (pts) with locally advanced breast cancer (LABC) relapse during first 5 years after treatment. The primary objective was to construct a prediction tool for risk of relapse (RR) in pts with LABC after neoadjuvant therapy (NAT).

Methods

This was single center, retrospective study of 546 pts with LABC who received NAT at the Ottawa Hospital Cancer Center between 2005 and 2015. Median follow-up was 49 months. The following data collected: demographics, tumor size, nodal and receptor status, grade, HER-2, stage, treatment and clinical outcomes. Primary endpoints were local and/or distant recurrence rate and time to relapse during the first 5 years. A prediction tool was devised based on the Cox regression model.

Results

Over 60 variables were included in primary analysis. Cox regression proportional hazards model analysis resulted in only 5 factors with significant influence on risk of relapse during first 5 years of follow up. Risk factors and their risk prediction value are: 1) residual disease (yes- 4; no-0), (HR = 4.25; p-value

Conclusions

Patients with LABC represent a heterogeneous group with diverse risk of disease recurrence that can be predicted. Patients with high risk may require additional treatment and/or more active follow-up strategies and this simple model may be used to design unique studies in LABC based on RP score. We intend to further validate this model on a larger multi center/provincial population.

Clinical trial identification

Legal entity responsible for the study

Olexiy Aseyev

Funding

None

Disclosure

All authors have declared no conflicts of interest.