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. 2025 Nov;14(21):e71325.
doi: 10.1002/cam4.71325.

Transcriptomic Diversity of Pediatric Acute Myeloid Leukemia Genetic Drivers Correlates With Clinical Outcome and Expression of Stemness-Related Genes

Quenton Rashawn Bubb  1   2   3 , Elena Sotillo  4 , Rebecca M Richards  5 , Crystal L Mackall  2   4   6 , Tanja A Gruber  2 , Agnieszka Czechowicz  2   3
Affiliations

Transcriptomic Diversity of Pediatric Acute Myeloid Leukemia Genetic Drivers Correlates With Clinical Outcome and Expression of Stemness-Related Genes

Quenton Rashawn Bubb et al. Cancer Med. 2025 Nov.

Abstract

Background: Pediatric acute myeloid leukemia (pAML) is comprised of a diverse set of oncogenic drivers (ODs) that have been risk-stratified to inform prognosis and therapeutic decision-making. Despite proteomic, transcriptomic, genetic, and epigenetic characterization of the pAML landscape, questions still remain about why certain ODs have poorer prognoses than others.

Methods: We analyze a large pAML bulk-RNA dataset (n = 435) and organize ODs along an axis of transcriptomic diversity by calculating the Simpson Diversity Index (SDI) of individual ODs.

Results: When comparing patients with low diversity ODs to patients with high diversity ODs, we observe poorer overall survival (HR = 1.877, 95% CI: 1.377-2.558, p = 0.0002) among patients harboring high diversity ODs in addition to an enrichment of stemness-related genes. We observe poorer survival of patients with high diversity ODs even when comparing patients with similar transcriptomic profiles (HR = 3.443, 95% CI: 1.817-6.525, p = 0.0028).

Conclusion: We identify a link between transcriptomic diversity, expression of stemness-related genes, and clinical outcome. Higher transcriptomic heterogeneity exhibited by high diversity ODs warrants further attention when identifying patients who can benefit from novel or high-intensity therapy.

Keywords: pediatric AML; transcriptomic diversity; transcriptomics.

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Conflict of interest statement

Q.R.B., E.S., R.M.R., C.L.M., and A.C. are inventors on a recently filed patent application related to separate CAR T‐related work. Additionally, A.C. discloses financial interests in the following entities working on antibody‐based conditioning approaches: Beam Therapeutics, Editas Medicines, GV, Inograft Biotherapeutics, Kyowa Kirin, and Prime Medicines. In addition, she is an inventor on antibody‐based conditioning patents licensed to Jasper Therapeutics, Gilead Sciences, Inograft Biotherapeutics, and Magenta Therapeutics. C.L.M. and E.S. are coinventors on multiple patents related to CAR T. C.L.M. is a cofounder, consults, and holds equity in CARGO Therapeutics, Link Cell Therapies, and GBM NewCo. C.L.M. consults for Ensoma and Immatics and received research funding from Tune and Lyell Immunopharma. E.S. consults for and holds equity in Lyell Immunopharma, and consults for Lepton Pharmaceuticals and Galaria.

Figures

FIGURE 1
FIGURE 1
Analysis of Transcriptomic Diversity in pAML—(A) Transcriptomic Diversity (1—Simpson Diversity Index) of 22 Oncogenic Driver categories analyzed in Fornerod et al. [2]. Error bars indicate the standard deviation of each calculation of the Simpson Diversity Index. Low Diversity drivers are colored in black, while high diversity drivers are colored in magenta. (B) Circos plot highlighting the transcriptomic identity composition of each oncogenic driver. (C) Circos plot highlighting the leukemic subtype composition of each oncogenic driver. (D) Overall survival of patients with low diversity oncogenic drivers and high diversity oncogenic drivers (p = 0.0002, Log‐rank (Mantel‐Cox) test). (E) Circos plot highlighting the oncogenic driver composition of each pLSC6‐based risk category [2]. (F) Comparison of pLSC6 scores between low diversity and high diversity oncogenic drivers (****p = 0.0002, unpaired t test). (G) HOX‐family and MEIS1 expression among low diversity and high diversity oncogenic drivers (****p < 0.0001, unpaired t test). (H) RBFOX2 and IRX1 expression among low diversity and high diversity oncogenic drivers (****p < 0.0001, unpaired t test). AMKL, acute megakaryoblastic leukemia; AML, acute myeloid leukemia; AUL, acute undifferentiated leukemia; B/M, B‐lymphoid and myeloid co‐expression; ETP, early T‐precursor; ITD, internal tandem duplication; MK, mixed karyotype; MPAL, mixed‐phenotype acute leukemia; Ph‐like, Philadelphia chromosome–like acute lymphoblastic leukemia; PTD, partial tandem duplication; T/B, T‐lymphoid and myeloid co‐expression; T/B/M, T‐lymphoid, B‐lymphoid, and myeloid co‐expression; T/M T‐lymphoid and myeloid co‐expression; Txn, transcription.
FIGURE 2
FIGURE 2
Diverse oncogenic drivers modify the probability of survival despite transcriptomic similarity. (A) Proportions of transcriptomic identities within low and high diversity oncogenic drivers (pie charts) and the distribution of patients across transcriptomic identities (bar plot). (B) Overall survival of patients with low diversity oncogenic drivers and high diversity oncogenic drivers (proportions shown in pie charts) (p = 0.0028, Log‐rank (Mantel‐Cox) test). (C) Overall survival of patients harboring NPM1, KMT2Ar, and NUP‐r in the entire pediatric cohort (p = 0.0056, Log‐rank (Mantel‐Cox) test). (D) Overall survival of patients harboring NPM1, KMT2Ar, and NUP‐r within the MK‐V transcriptomic cluster (p = 0.0002, Log‐rank (Mantel‐Cox) test). (E) HOX10 and HOPX expression among patients harboring NPM1, KMT2Ar, and NUP‐r within the MK‐V transcriptomic cluster (****p < 0.0001, One‐way ANOVA). (F) HOPX expression among patients harboring KMT2A rearrangements across transcriptomic identities (**p = 0.0037, ***p = 0.0002, One‐way ANOVA). (G) HOPX expression among patients harboring NUP rearrangements across transcriptomic identities (****p < 0.0001, One‐way ANOVA).
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