Frameshift Mutations in Leukemia-Associated Genes Correlate With Superior Outcomes in Patients Undergoing Allogeneic Stem Cell Transplant for <i>De Novo</i> Acute Myeloid Leukemia
Bibliographic record
Abstract
Background: Allogeneic stem cell transplant (allo-SCT) is a mainstay of treatment for acute myeloid leukemia (AML). Its success depends largely on response of donor T lymphocytes against leukemia cells, known as graft-vs-leukemia (GvL) effect. A key potential driver of GvL is immune response to mutation-derived neoantigens. Previous studies in solid tumors have demonstrated enhanced immunogenicity of frameshift (FS)-derived peptides vs. those from non-synonymous single nucleotide variants (SNVs). We therefore hypothesized that AML cases bearing FS mutations in leukemia-associated genes would be more immunogenic than those with only other types of mutations (non-FS), and thus benefit more from allo-SCT via more robust GvL. Methods: We identified AML patients who had undergone allo-SCT between 2010 and 2022 and had next-generation sequencing data available on diagnostic specimens using a 42-gene hot spot panel. We compared the impact of tumor mutations present at diagnosis on overall survival and relapse-free survival based on FS versus non-FS status. Results: Ninety-five AML allo-SCT patients were identified. We observed superior relapse-free survival (P = 0.038, hazard ratio (HR): 0.24) and borderline superior overall survival (P = 0.058, HR: 0.55) post-transplant in de novo AML patients, who had at least one FS mutation (other than NPM1 ) in one of the 42 assessed genes versus those with only non-FS mutations. Conclusions: Our findings suggest that FS-mutated AML cases may benefit more from allo-SCT than those with only non-FS mutations, possibly due to increased generation of immunogenic neoepitopes. If validated in an expanded study, incorporation of somatic FS mutation status in AML could improve patient selection algorithms for bone marrow transplant and thereby lead to superior outcomes. J Hematol. 2024;13(3):86-93 doi: https://doi.org/10.14740/jh1276
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".