High-Dimensional Single-Cell Analysis of Clinical-Grade Flow Cytometry Explains the Unpredictable Complex Shifts of Patient-Specific Immunophenotypes during B-ALL Relapse Progression
Bibliographic record
Abstract
Relapsed precursor B-cell ALL (B-ALL) commonly evolves from minor diagnostic clones, but their early phenotypic characterization remains unachieved despite abundant multi-omics data. Immunophenotyping, an essential diagnostic and therapy-response monitoring tool, identifies the patient-specific expression pattern of bulk leukemia. At time of relapse, most B-ALL immunophenotypes have undergone substantial and seemingly random multi-directional modulations of their diagnostic expression pattern. For decades, these unpredictable immunophenotypic shifts at time of relapse, observed across different methods of antigen expression assessment, have prevented a deeper understanding of phenotypically-defined leukemia progression biology. Addressing this limitation, we applied unsupervised high-dimensional computational analysis of clinical-grade flow cytometry to dissect the intra-leukemic phenotypic heterogeneity at the single-cell level in longitudinally collected B-ALL patients and matched patient-derived xenografts. Our results provide AI-guided and clinically validated evidence that the observed immunophenotypic shifts during disease progression did not result from antigen expression fluctuations, but from enrichment of distinct phenotypically stable subpopulations. As our study identifies patient-specific subpopulation dynamics during disease evolution, it achieves immunophenotypically-defined leukemia progression assessment, which addresses an important unmet clinical and translational need. In each progression series, population dynamics followed a trajectory towards relapse-dominating subpopulations when selective pressures, such as xenotransplantation or in vivo chemotherapy, were applied, often from very minor abundance levels at diagnosis. Each time, the changes in relative proportions of subpopulations explained the observed immunophenotypic shift at the bulk-level. Overcoming decades-old challenges, our findings provide a new conceptual approach to investigate the role of intra-leukemic phenotypic heterogeneity in B-ALL progression to identify treatment-refractory phenotypes, which could significantly impact patient-care, inform precision-medicine options, and enhance relapse-modelling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".