Rodrigues, Leonardo O.

Abstract Background: Cyclin-dependent kinase 4/6 inhibitors (CKD4/6i) have demonstrated clinical utility extending progression-free survival (PFS) and overall survival (OS) for advanced hormone receptor positive and HER2 negative (HR+/HER2-) breast cancer patients. The efficacy in early-stage breast cancer (eBC) is unclear, with conflicting results from adjuvant CDK4/6i trials on invasive disease-free survival. Thus, there is a critical need to identify biomarkers of response (BoR) to determine which, if any, eBC patients could benefit from this treatment. This BoR could also stratify advanced BC patients for likelihood to respond to CDK4/6i. Metabolism is influenced by both genome and environment, and changes in the metabolome can be correlated with drug responsiveness. Thus, metabolite BoRs may serve to identify eBC patients for which CDK4/6i would offer a therapeutic benefit.Methods: Plasma and serum samples from 50 early-stage ER+/HER2- breast cancer patients, treated with neoadjuvant CDK4/6 inhibitor palbociclib (palbo) and aromatase inhibitor (AI) anastrozole on NeoPalAna trial (ClinicalTrials.gov identifier NCT01723774), were collected from treatment-naïve patients (BL) and 3 consecutive time points: anastrozole,1 mg daily for 4 weeks (C1D1), anastrozole plus palbo,125 mg daily, for 15 days (C1D15), and for 4-5 months before surgery (SURG). Metabolites were extracted from all samples via methanol and chloroform precipitation and quantified using an unbiased, non-destructive, nuclear magnetic resonance (NMR)-based profiling platform (Olaris®, Inc., Waltham, MA). Statistical analysis and machine learning was used to identify differential metabolites and generate predictive models. A separate validation set of samples was collected from a subset of patients (N=6) who received an additional cycle of palbo treatment prior to surgery to assess model accuracy. Results: Non-parametric differential expression analysis of BL/C1D1, BL/C1D15, and C1D1/C1D15 identified 53 ,97, and 90 differential NMR resonances in plasma (p<0.05) and 36, 34, and 25 differential NMR resonances in serum (p<0.05), respectively. Based on the proliferative marker Ki67 levels at C1D15, 37 patients were classified as responders (Ki67≤2.7%) and 6 patients as non-responders (Ki67>2.7%). Analysis of the responder (R) and non-responder (NR) groups identified that 13 plasma and 14 serum resonances (21 unique resonances and 6 overlapping) were differentially expressed (p<0.05) at C1D1. Many of the differential resonances could be mapped back to amino acid metabolites including several branched chain amino acids such as leucine, valine, and isoleucine, and positively charged amino acids such as lysine. A Olaris® BoR score was generated using 5 differential resonances that had an AUC of 0.931 (training set) and 100% accuracy when predicting palbo-response in a blinded test set (N=6).Conclusion: The differential metabolites identified from matching plasma and serum samples suggest that, compared to serum, plasma has a better representation of the metabolic changes associated with palbo treatment-response. While comparing samples from R and NR patients, amino acids were found to be consistently altered in both serum and plasma before palbo treatment. In addition, a BoR model based on select metabolites could precisely stratify palbo-response in a blinded dataset. A larger independent validation cohort is ongoing. Citation Format: Chen Dong, Shana Thomas, Chandrashekhar Honrao, Leonardo O. Rodrigues, Nathalie Tessier, Bo Zhang, Souzan Sanati, Kiran Vij, Brenda J. Ernst, Karen S. Anderson, Mateusz Opyrchal, Foluso Ademuyiwa, Lindsay L. Peterson, Matthew P. Goetz, Donald Northfelt, Elizabeth O'Day, Cynthia Ma. Identifying a metabolite signature that correlates with tumor proliferation in early-stage breast cancer patients treated with CDK4/6 inhibitors from matched plasma and serum samples [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-13-20.

723 Background: BPM31510-IV is an Ubidecarenone (CoQ10) drug-lipid conjugate nanodispersion targeting metabolic machinery in cancer, shifting bioenergetics from lactate dependency towards mitochondrial OxPhos to generate ROS and activate apoptosis. An MTD of BPM31510-IV in combination with gemcitabine was established at 110mg/kg in a Phase I clinical trial, which determined the dose for the Phase 2 investigation. Methods: Eligible patients (aged ≥ 18 y) with relapsed/refractory PDAC to standard treatment (ST) and met inclusion/exclusion criteria were recruited. Patients received 110mg/kg IV BPM31510 in combination with gemcitabine in a 144-hour infusion. Tumor response was evaluated at week 10 and then every 8 weeks. Study endpoints assessed were Overall Response Rate (ORR), Overall Survival (OS), Progression-Free Survival (PFS), Time to Progression (TTP), Tumor Response using Adaptive Molecular Responses (multi-omic molecular profiling), changes in CA 19-9 levels and patient reported Quality of Life (QOL) using the validated FACT-HEP PRO. A comprehensive multi-omic profiling for identification of biomarkers for patient stratification was explored. Results: Of the 35 patients enrolled to receive therapy, 18 patients met criteria of an adequately treated cohort (ATC- received BPM31510-IV + gemcitabine for 30 days over 2 cycles and had a RECIST 1.1 evaluation) while remaining (n = 17) had progressive disease (PD). Half of the ATC population (n = 9/18, 50%) achieved best ORR of stable disease (SD); 10/18 (55 %) demonstrated SD as best response at target lesions and 8/18 demonstrated SD at end of Cycle 2. The mTTP was 121 days (70 – 147, 95% CI); PFS 118 days (70 – 131, 95% CI) and OS 218 days (131 – 228, 95% CI), respectively. Overall, BPM31510-IV was well tolerated; the most common AE’s were GI related. Conclusions: The efficacy signal observed in this heavily pretreated population in addition to the toxicity profile warrants further clinical investigation of BPM31510-IV + gemcitabine in advanced PDAC. Clinical trial information: NCT02650804 . [Table: see text]

Abstract Pancreatic adenocarcinoma generally presents with a poor prognosis and an extremely low response rate to first line therapies. There is a critical unmet need to discover and implement effective diagnostic panels to stratify outcomes and tailor treatment strategies to improve survival. The BERG Interrogative Biology® platform utilizes Artificial Intelligence to analyze and integrate multi-omic profiles with clinical annotation to define novel biomarkers and improve treatment interventions. Herein, we analyzed the serum proteome, signaling lipidome, structural lipidome, and metabolome of 163patients at multiple timepoints (pancreatic cancer: 115; pancreatitis: 15; and age-matched healthy controls: 33). Utilizing the power of the Bayesian Network learner, bAIcis™ (BERG Artificial Intelligence Clinical Information System), multi-omic profiles were aligned to the longitudinal clinical information and subjected to AI-algorithms to infer probabilistic cause-and-effect relationships among molecular and clinical variables explicitly explaining pancreatic cancer status, cancer progression, and survival, and defining the interconnectivity of molecular features with clinical phenotype. Network features linking into clinical endpoints and key network pressure points were identified as molecular drivers. The drivers of clinical endpoints were analyzed to rank potential biomarkers. Novel biomarkers were discovered that demonstrate diagnostic potential for pancreatic cancer detection, disease prognosis including metastatic disease progression, patient stratification associated with survival, and response to standard chemotherapy agents like Gemcitibine. A prospective study is underway to validate the biomarkers and discover additional diagnostic and therapeutic molecules to improve the outcomes for patients affected by adenocarcinoma of the pancreas. Citation Format: Niven R. Narain, A James Moser, Ramesh Ramanathan, John Crowley, Amy Stoll-D’Astice, Yezhou Sun, Leonardo O. Rodrigues, Eric M. Grund, Emily Chen, Vivek K. Vishnudas, Michael A. Kiebish, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan. Project Survival: Interrogative Biology® platform mediated discovery of molecular markers for detection, stratification and outcomes in pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 786.