Kester, L. | bioRxiv Science

bioRxiv2026

The bone marrow microenvironment of RAS pathway mutant B-ALL is enriched for immunosuppressive regulatory T cells

Somatic mutations in the RAS pathway are highly prevalent in B-Cell Acute Lymphoblastic Leukemia (B-ALL), yet their impact on the bone marrow immune microenvironment and response to immunotherapy remains poorly defined. In this study, we integrated bulk RNA-sequencing, single-cell RNA-sequencing (scRNA-seq), and spectral flow cytometry to characterize the immune landscape of RAS-mutant B-ALL. We identified pathogenic mutations in KRAS, NRAS, PTPN11, or BRAF in 42% of the cohort, predominantly as clonal events. Despite similar T-cell frequencies by flow cytometry, bulk transcriptomes from RAS-mutant samples showed suppression of immune-response and T-cell-activation pathways, and T cells from RAS-mutant patients exhibited impaired proliferation ex vivo. Single-cell analysis revealed higher CD8 dysfunction scores and enrichment of regulatory T cells (Tregs) in RAS-mutant bone marrow. These findings were validated by spectral flow cytometry and by CIBERSORTx deconvolution of bulk data. Trajectory analysis supported a higher CD4 to Treg differentiation in the RAS-mutant niche, and CellChat mapping identified contact-dependent and checkpoint interactions (including TIGIT-NECTIN2 and CTLA-4-CD86/ICOSL) enriched in RAS-mutant samples. Functionally, blinatumomab produced limited leukemic-cell killing ex vivo overall, but addition of CTLA-4 blockade (ipilimumab) selectively restored blinatumomab efficacy in RAS-mutant samples. Together, these results indicate that RAS-pathway activation associates with a Treg-enriched, immunosuppressive bone-marrow microenvironment and point to CTLA-4-targeted strategies to enhance T-cell-engager efficacy in this subgroup.

cancer biology↗

bioRxiv2026

ALLCatchR, a machine-learning classifier identifies now 20 T-ALL subtypes across cohorts and age groups

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2,314 transcriptomes (15 cohorts, age: 0.8 to 90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (ETP-like) definition and resolved the LMO2{gamma}{delta} -like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine learning classifier based on ALLCatchR, our B-ALL classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n=203). Testing the classifier on a second hold-out data set (n=265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene expression cluster markedly enriched (P<0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts representing 8.9% of adults and 39.5% of patients aged >50 years. We advanced ALLCatchR, as a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts. Key PointsO_LIEstablished using 2,314 T-ALL transcriptomes, ALLCatchR2 assigns 20 RNA-Seq subtypes and their developmental underpinnings across ages. C_LIO_LIClonal hematopoiesis-related T-ALL (DNMT3A, IDH2 R140Q) defines an immature gene-expression cluster in [~]40% of patients aged >50 years. C_LI

bioinformatics↗