Abstract CT130: Molecular-biological tumor profiling for drug treatment selection in patients with advanced and refractory carcinoma: A prospective, two-stage Phase II individualized cancer treatment study

Abstract

Abstract BACKGROUND: The identification of predictive biomarkers is the basis of individualized cancer treatment. To this end, non-invasive molecular profiling of circulating tumor DNA (ctDNA) from peripheral blood could eliminate ineffective therapy attempts. In this prospective, non-randomized, open two-stage clinical phase II study (EudraCT number: 2014-005341-44), the success of a targeted therapy selected by molecular tumor profiling was evaluated. Patients with locally advanced or metastasized carcinoma were recruited at the Division of Oncology at the Medical University of Graz. The primary endpoint was set as a 1.2-fold increase of PFS compared to the last evidence-based drug therapy. METHODS: Whole-genome sequencing (0.1x coverage) and a cancer hotspot panel of clinically relevant genes were performed on plasma DNA to identify clinically relevant somatic copy number alterations (SCNAs) and mutations. To match the genetic profile to targeted treatments, publicly available databases such as “My Cancer Genome” were used. Annotated results were discussed at a molecular tumor board including oncologists, pathologists and a clinical geneticist. Additionally, selected molecular profiles were analyzed retrospectively via CureMatch PreciGENE™ to validate our results and to test the algorithm for its potential to identify combinatorial therapy options. RESULTS: In accordance with the study protocol, an interim analysis was performed after 24 patients. Median age was 56 years (range 38-74). Colorectal cancer (n=7) comprised the majority of the tumor entities evaluated whereas pancreas (n=4), cholangiocarcinoma (n=2), cardiac (n=2), CUP (n=2), renal (n=1), gallbladder (n=1), breast (n=1), laryngeal (n=1), gastric (n=1), esophageal (n=1) and bladder (n=1) comprised the remaining number of cases in this pan-cancer cohort. Informative results could be achieved in 18 patients (75%), meaning that either an SCNA or mutation was detected and that tumor-specific DNA content was above 5% (median tumor fraction 22.7%, range 5.2-40.3). Of these, 8 patients had a molecular target associated with a current existing drug. Furthermore, PreciGENE™ analysis was able to identify either a 2-drug or 3-drug combination option in each of the tested cases, including a case for which no therapy was previously identified. CONCLUSIONS: Statistical analysis of the molecular tumor board decisions and patient outcomes is now ongoing. However, early evidence demonstrated that only a low number of patients benefited from this approach. Retrospective analysis of select cases via the CureMatch PreciGENE™ platform was able to suggest combination therapies overlooked by standard database matching of actionable targets with existing therapies, suggesting the power of a machine learning approach to finding evidence-based therapies and potentially improving patient outcomes. Citation Format: Samantha O. Perakis, Peter Ulz, Jakob M. Riedl, Lukas Scheipner, Karl Kashofer, Gerald Hoefler, Jochen B. Geigl, Michael R. Speicher, Ellen Heitzer, Armin Gerger. Molecular-biological tumor profiling for drug treatment selection in patients with advanced and refractory carcinoma: A prospective, two-stage Phase II individualized cancer treatment study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT130.