LC3AB protein-based gene (PbG) signatures as an indicator of relapse even after achieving pathological complete response.

Abstract

e12552 Background: The application of tissue-based quantitative protein and morphological profiling of breast cancer tissues combined with RNA sequencing provides a flexible and generally applicable and scalable method to develop Protein-based Gene (PbG) signatures with significant prognostic value. Methods: A racially and subtype diverse breast cancer cohort (N=555) with available subset of RNA-seq data was used to develop gene expression-based signatures by stratifying patients according to protein expression (IHC) based on survival information and tumor lymphocyte infiltration (TILs) using survival-derived cutoff values. The predictive value of these signatures was then compared with other popular biomarkers scoring systems such as ROR.score, and OncotypeDx (Gene 21). Here we describe the predictive value of these signatures is evaluated by logistic regression modeling of publicly available gene expression data (N=996) from patients receiving neoadjuvant chemotherapy (anthracycline +/- taxane) with a final validation in an independent cohort (N=443) receiving similar therapy. Results: A model of PbG-derived signature from Kaiso, LC3A/B IHC and quantitative TIL-measurements together with ROR score, MAPK, PTEN, Immune and Gene21 signatures show significant prediction of distant metastasis-free survival in all patients: (DMFS, AUC= 0.737, 95% CI: 0.665- 0.819); and in non-pCR patients DMSF (AUC= 0.71, 95% CI: 0.633-0.773). When examining the role of all those markers among the patients who achieved the pCR and had relapse, the LC3A/B PbG-derived score was found to be highly predictive of those patients who would not achieve pCR and ultimately have recurrent disease. Conclusions: PbG-based signatures utilizing on LC3A/B protein values offer a novel and clinically relevant predictive biomarker of response to therapy and relapse in breast cancer patients.