Abstract A031: Prospective and clinical prediction in a retrospective trial that experimentally validated an AI/ML-derived whole-genome predictor as the most accurate and precise predictor of survival and response to treatment in glioblastoma

Abstract Cancer is complex, with contributing factors distributed across the entire genome affecting every aspect of the disease. But typical artificial intelligence and machine learning (AI/ML) would require 3B-patient training sets to generate predictive models from the whole 3B-nucleotide genome. As a result, tests remain limited to one to a few hundred genes. Prediction continues to rely mostly on such factors as a tumor’s grade and the patient’s age. And the understanding and management of cancer continue to involve guesswork. A genome-wide pattern in tumors from glioblastoma (GBM) patients was recently experimentally validated in a retrospective clinical trial as the most accurate and precise predictor of life expectancy and response to standard of care doi: 10. 1063/1. 5142559. Applicable to the U. S. population at large, this predictor, the first to encompass the whole genome, was mathematically (re) discovered and computationally (re) validated in open-source datasets from as few as 50–100 patients by using our data-agnostic physics-inspired AI/ML doi: 10. 1063/1. 5099268, 10. 1073/pnas. 0530258100. All other attempts to connect a GBM patient’s outcome with the tumor’s DNA copy numbers failed. For 70 years, the best indicator has been age. At 75–95% accuracy, our predictor is more accurate than and independent of age and all other indicators, including the one-gene tests for MGMT, IDH1, and TERT. Platform- and reference genome-agnostic, the predictor’s 99% precision is greater than the community consensus of 70% reproducibility based upon one to a few hundred genes. It describes mechanisms of transformation and identifies drug targets and combinations of targets to sensitize tumors to treatment. Now, in follow-up results from the trial we, first, show correct prospective prediction of the outcome of the five of the 79 patients who were alive four years earlier, at the time of first results. Two patients, who were predicted to have shorter survival, lived less than five years from diagnosis, whereas of the three patients predicted to have longer survival, one lived more than five, and the remaining two are alive 11. 5, years from diagnosis. Second, we demonstrate 100%-precise clinical prediction for the 59 of the 79 patients with remaining tumor DNA, by using whole-genome sequencing in a Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologists (CAP) -regulated laboratory. Third, we establish that the risk that a tumor’s whole genome confers upon outcome, as is reflected by the predictor, among the 79 and, separately, 59 patients, is greater than that conferred by the patient’s Karnofsky performance score and access to chemotherapy and is surpassed only by the patient’s access to radiotherapy. This is a proof of principle that our AI/ML is uniquely suited for personalized medicine, that a patient’s survival and response to treatment are the outcome of their tumor’s whole genome, and that our AI/ML-derived whole-genome predictors can take the guesswork out of standard of care, clinical trials, and drug development. Citation Format: Sri Priya Ponnapalli, Penelope Miron, Kristy L. S. Miskimen, Kristin A. Waite, Nadiya Sosonkina, Sara E. Coppens, Anthony C. Bryan, Estevan P. Kiernan, Huanming Yang, Jay Bowen, Ghunwa A. Nakouzi, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Tiffany R. Hodges, Orly Alter. Prospective and clinical prediction in a retrospective trial that experimentally validated an AI/ML-derived whole-genome predictor as the most accurate and precise predictor of survival and response to treatment in glioblastoma abstract. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84 (5 Suppl₁): Abstract nr A031.