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Abstract Background Cyclin Dependent Kinase inhibitors (CDKi’s) in combination with Endocrine Therapy (ET) have changed the first line management of patients with the most common subtype (HR+, HER2-) of metastatic breast cancer (MBC), improving progression-free survival (PFS) and overall survival. However, in approved indications for advanced breast cancer, approximately 20% of patients have been observed to have no response to CDKi’s and ET, and to progress after starting these therapies. Unlike other targeted therapeutics, there is no companion diagnostic or other clinically reliable biomarker for clinical decision support to indicate which patients will and will not benefit from CDKi therapy. Methods We have developed a novel platform that measures the Immune Activation Signature of patient plasma samples. Via simple measurements of the total concentration of protein-incorporated amino acid residues within patient plasma samples, the platform is designed to reveal changes in the proportion of immunoglobulins and albumin, and class-switching among immunoglobulins. For that purpose, we use Bioorthogonal chemical labelling reactions to label the protein-incorporated amino acids residues within the plasma. We measured the plasma samples of N=30 HR+, HER2- MBC patients, median age 54 years old, who were baseline naïve for CDKi’s treatment (16 on palbociclib, 11 on ribociclib, and 3 on abemaciclib) and ET. Eighteen patients had metastatic visceral disease and 12 patients had bone-only MBC. All patients were prospectively followed, and response assessed according to RECIST criteria on a CT scan every 3-months. Results Non-responding patients developed objective progressive disease during the first 6 months after starting CDKis and ET. In this cohort, 4 patients (13%) were classified as non-responders. The median PFS in non-responding patients was 4.5 months (4.03-5.02) and the median PFS in the cohort of responding patients was 16.6 months (6.8 – 40.9). The Immune Activation Signatures of the Responding and Non-Responding patients are shown in table 1. We analyzed the results with classical statistics and machine learning. A multivariate analysis of variance (MANOVA) test of the null hypothesis that the Immune Activation Signatures of Non-Responders and Responders are the same gave p = 0.00388. We analyzed the measured Immune Activation Signatures using a supervised machine learning classifier and observed in a held-back validation set correct prediction of 100% of non-responding patients and 95% accurate predictions overall. Conclusions According to these results, this platform may provide a clinically helpful biomarker for clinical decision support in the advanced or aggressive breast cancer context (patients with visceral metastasis bordering on visceral crisis), or for patient stratification in new indications. Table. Immune Activation Signature measurements (mean values in Clinical Outcome) Citation Format: Luis Costa, Cong Tang, Emma Yates, Qi Shi, Wesley Sukdao, Patricia Corredeira, Gonçalo Costa, Helena Pais, Catarina Abreu, Leonor Ribeiro, Rita Teixeira de Sousa, Sofia Torres, André Mansinho, Sandra Casimiro, Ana Cavaco, Patricia Alves, Angela Rodrigues, Lisiana Szeneszi, Gonçalo Bernardes. Immune Activation Signatures for predicting CDKi primary response in advanced breast cancer patients abstract. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-05-05.
Costa et al. (Thu,) studied this question.