Endocrine therapy plus CDK4/6 inhibition is the standard of care for the treatment of patients with ER-positive HER2-negative breast cancer on the first-line metastatic setting. However, these treatments mostly fail and patients acquire resistance to the to CDK4/6 inhibition plus endocrine therapy during time. We now know that we can identify genetic mechanisms for resistance in about 40-50% of patients, however, in the other 50%, we don't know which are the genetic basis of this resistance. With that in mind, we try to use novel methods, including mutational signature analysis, to study patterns of endocrine resistance. Mutational signatures allow us to study not only a single mutation, but also the mutational process that may confer resistance to these treatments. For our study, we use the machine learning basic tool called SigMA to predict mutational signature for target panel sequencing, and then we studied this signature. We evaluate the role of this signature on the outcomes of patients treated with CDK4/6 inhibitors plus endocrine therapy. We found that two signatures, mainly the APOBEC and HRD that are leading to genomic instability, were associated with reduced benefit on CDK4/6 inhibitors plus endocrine therapy. This may allow us, in the future, to try to use this tool to create some personalized treatment for this patient. If for HRD we already have some drugs, including PARP inhibitors, novel DDR agent that can be used to overcome this resistance and we are not sure how to understand which is the best setting to test this drug in patient with ER-positive HER2-negative tumors. For APOBEC, this is something that's challenging at the moment because we don't know how to better target these mutational processes in breast cancer, but the results for our work pave the way to further investigate specific treatment that can target APOBEC in ER-positive HER2-positive endocrine-resistant tumors.
