Presentation
AI/ML-Derived Whole-Genome Predictor Prospectively and Clinically Predicts Survival and Response to Treatment in Brain Cancer
DescriptionCancer 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 glioblastoma brain cancer tumors was experimentally validated in a retrospective clinical trial as the most accurate and precise predictor of life expectancy and response to standard of care [1]. Applicable to the general population, this predictor, the first to encompass the whole genome, and predictors in lung, nerve, ovarian, and uterine cancers, were mathematically (re)discovered and computationally (re)validated in open-source datasets from as few as 50–100 patients by using our AI/ML [2,3]. Data-agnostic, our algorithms, multi-tensor comparative spectral decompositions, extend the mathematics that underlies quantum mechanics to overcome typical AI/ML obstacles by not requiring large amounts of data, balanced data, or feature engineering. All other attempts to connect a glioblastoma 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. 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 for 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 regulated laboratory. Third, we establish that the risk that a tumor’s whole genome confers upon outcome, as is reflected by the predictor, 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. This also demonstrates that the inclusion of complete genomes, and the normal diversity within, is, beyond fair AI/ML, a scientific, engineering, and medical necessity, because a patient’s survival and response to treatment are the outcome of their tumor’s whole genome. We conclude that our AI/ML-derived whole-genome predictors can take the guesswork out of cancer.
[1] Ponnapalli et al., APL Bioeng 4, 026106 (2020); https://doi.org/10.1063/1.5142559
[2] Bradley et al., APL Bioeng 3, 036104 (2019); https://doi.org/10.1063/1.5099268
[3] Alter et al., PNAS 100, 3351 (2003); https://doi.org/10.1073/pnas.0530258100
A genome-wide pattern in glioblastoma brain cancer tumors was experimentally validated in a retrospective clinical trial as the most accurate and precise predictor of life expectancy and response to standard of care [1]. Applicable to the general population, this predictor, the first to encompass the whole genome, and predictors in lung, nerve, ovarian, and uterine cancers, were mathematically (re)discovered and computationally (re)validated in open-source datasets from as few as 50–100 patients by using our AI/ML [2,3]. Data-agnostic, our algorithms, multi-tensor comparative spectral decompositions, extend the mathematics that underlies quantum mechanics to overcome typical AI/ML obstacles by not requiring large amounts of data, balanced data, or feature engineering. All other attempts to connect a glioblastoma 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. 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 for 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 regulated laboratory. Third, we establish that the risk that a tumor’s whole genome confers upon outcome, as is reflected by the predictor, 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. This also demonstrates that the inclusion of complete genomes, and the normal diversity within, is, beyond fair AI/ML, a scientific, engineering, and medical necessity, because a patient’s survival and response to treatment are the outcome of their tumor’s whole genome. We conclude that our AI/ML-derived whole-genome predictors can take the guesswork out of cancer.
[1] Ponnapalli et al., APL Bioeng 4, 026106 (2020); https://doi.org/10.1063/1.5142559
[2] Bradley et al., APL Bioeng 3, 036104 (2019); https://doi.org/10.1063/1.5099268
[3] Alter et al., PNAS 100, 3351 (2003); https://doi.org/10.1073/pnas.0530258100
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