Discovery from single-cell RNA sequencing profiles of 18 CPTAC glioblastoma patients and validation in bulk profiles of 138 TCGA patients and two human-derived cell lines of a whole-transcriptome predictor of overall survival and drug targets by using quantum mechanics–based AI/ML.
Abstract
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Background:
Single-cell, more than bulk, multi-omic data, are small-cohort, noisy, and high-dimensional, and extremely difficult to model. We have developed artificial intelligence and machine learning (AI/ML) to overcome these challenges [doi: 10.1073/pnas.0530258100]. We have shown that our algorithms are uniquely able to discover accurate, precise, actionable, and mechanistically interpretable predictors, applicable to the general population, from the bulk multi-omes of as few as 19 patients [doi: 10.1158/1538-7445.AM2025-CT227]. We have demonstrated that these predictors of overall survival (OS) and drug targets consistently validate across laboratories and sometimes across indications, in federated and imbalanced studies and over time, and outperform all others where they exist [doi: 10.1063/1.5099268, 10.1200/JCO.2024.42.16_suppl.10043]. Here, we demonstrate our AI/ML in single-cell data.
Methods:
We used our algorithms to derive models from the single-cell RNA sequencing profiles of 18 glioblastoma (GBM) Clinical Proteomic Tumor Analysis Consortium (CPTAC) patients, and tested them in the bulk profiles of 138 patients in the Cancer Genome Atlas (TCGA). CPTAC and TCGA both profiled the core primary tumor of each patient, but CPTAC also sampled the peritumoral brain tissue. The two cohorts are indistinguishable in terms of their gender, age, and OS distributions, but they significantly differ in terms of race.
Results:
A whole-transcriptome model was discovered…
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: 138 participants or records.
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