Abstract 1134P
Background
Breast cancer (BC) is the world's most commonly diagnosed cancer type among women. Recent clinical evidence suggests that CDK4/6 inhibitors (CDK4/6i) improve the outcomes across different subtypes of breast cancer. Based on these findings, the U.S. Food and Drug Administration (FDA) in February 2015 granted accelerated approval for palbociclib, ribociclib, and eventually abemaciclib for treatment of estrogen receptor-positive (ER+) human epidermal growth factor receptor-2-negative (HER2-) BC. Despite many studies supporting the use of CDK4/6i in ER+/HER2- BC, administration of these drugs is still limited, mainly due to relatively high occurrence of side effects and the current price of the therapy in comparison to endocrine monotherapy treatment. Currently, despite ongoing research, the field of cancer research still lacks reliable clinical biomarkers of response (i.e., predictive biomarkers) for CDK4/6-targeted therapeutics.
Methods
In our study we have applied approaches, such as machine learning algorithms bundled with molecular biology and genomics, enabling patient stratification and prediction of therapy responses for CDK4/6i treatment options. We have performed a retrospective analysis of CDK4/6i response, using our proprietary genomic database, consisting of over 1,000 genomes derived from BC patients, 171 BC patients of which were treated with CDK4/6i (i.e., palbociclib and ribociclib).
Results
The Random Forest-based AI algorithm achieved good performance on nine genomic features that were selected as the most biologically relevant. Hold-out validation, a performance metric of the CDK4/6iDx classifier, provided a discriminative power of 83.88% by area under receiver operating characteristic (AUROC) curve analysis. Consistently, the low rate of misclassifications (high precision and recall of 90% and 88%, respectively) demonstrates the high discriminating power of the final classifier algorithm.
Conclusions
These results show that the CDK4/6i genomic signature, consisting of 9 biomarkers, improves the predictive stratification of ER+/HER2- metastatic BC patients, increasing the chance of identifying patients that will experience clinical benefit from CDK4/6i therapy.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
MNM Diagnostics.
Disclosure
All authors have declared no conflicts of interest.