Quantum mechanics–based multi-tensor AI/ML discovery and validation of actionable and mechanistically interpretable whole-transcriptome predictors of survival in response to immunotherapy from real-world clinical trial data
Abstract
2621
Background:
Prediction in cancer remains limited, and 90% of drugs continue to fail trials and post-market validation. The entire multi-ome affects the disease. Previously, we developed quantum mechanics-based multi-tensor AI/ML to overcome the limitations of typical AI/ML, e.g., neural networks and deep learning, in small-cohort, noisy, high-dimensional, multi-omic clinical data [doi:
10.1073/pnas.0530258100
,
10.1145/3624062.3624078
]. We have demonstrated the algorithms in the discovery and validation of whole-genome and -chromosome predictors of survival and response to treatment in, e.g., brain, lung, ovarian, and uterine cancers [doi:
10.1063/1.5142559
,
10.1200/JCO.2024.42.16_suppl.10043
].
Methods:
Here, we use the algorithms to discover two whole-transcriptome predictors of OS in response to atezolizumab PD-L1 inhibitor immunotherapy in a 348-patient, multi-center, single-arm, bladder cancer clinical trial, and validate the predictors in the 401-patient bladder cancer cohort in the Cancer Genome Atlas (TCGA).
Results:
The algorithms discovered the two predictors in the open-source, pre-atezolizumab, locally advanced or metastatic disease profiles of the 348 patients alone. By incorporating the patient labels, both predictors were found to outperform the best indicator of response to the treatment to date, i.e., the tumor mutation burden (TMB): The Cox proportional hazards model ratios, i.e., the corresponding relative risks, of 2.7 and 1.7, and concordance…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 348 participants or records.
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