Targeting immune checkpoints has achieved great therapeutic effects in the treatment of early-stage tumors. However, most patients develop adaptive resistance to this therapy. The latest evidence demonstrates that tumor-derived exosomes may play a key role in systemic immune suppression and tumor progression. In this article, we highlight the role of exosomal immune checkpoint proteins in tumor immunity, with an emphasis on programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), as well as emerging evidence on roles of T cell immunoglobulin-3 (TIM-3), arginase 1 (ARG1), and estrogen receptor binding fragment-associated antigen 9 (EBAG9) expressed by exosomes.

Owing to the recent emergence of drug resistance to Bruton’s tyrosine kinase inhibitors (BTK) in chronic lymphocytic leukemia (CLL) treatment, it is crucial to identify alternative therapeutic targets. Therefore, we aimed to identify therapeutic options for CLL besides BTK. We identified that HIF1A expression was higher in CLL patients than in controls, which may suggest good prognosis. We used a lentiviral knockdown of EGLN1 (encoding hypoxia-inducible factor prolyl hydroxylase [HIF-PH]) and found that the growth of MEC-1 cells slowed in the knockdown group. Treatment of CLL cell lines MEC-1 and HG3 with the HIF-PH inhibitor molidustat showed that molidustat could induce apoptosis in a concentration-dependent manner in CLL cells and had low cytotoxicity at this concentration. CXCR4, HIF1A, SLC2AI, and VEGF, the downstream molecules of the HIF pathway, were upregulated after molidustat treatment. Western blotting results indicated that molidustat increased HIF1A expression in CLL cell lines and cells from CLL patients, and sequencing/quantitative PCR analysis demonstrated that the ribosome biogenesis pathway was inhibited in MEC-1 cells after molidustat treatment. We further identified synergistic cytotoxicity of molidustat in combination with ibrutinib on the MEC-1 and HG3 cell lines at certain concentrations. Therefore, molidustat is a potential therapeutic option for CLL.

Adult B-cell acute lymphoblastic leukemia (B-ALL) is clinically heterogeneous, and established prognostic markers such as minimal residual disease (MRD) do not fully explain survival differences. Untargeted metabolomics may capture biologic states linked to outcome, but it is not routinely available in clinical practice. Here, we developed a plasma metabolite-based risk model for overall survival (OS) and translated it into a transcriptomic surrogate for RNA-only cohorts. Using newly diagnosed patients with plasma metabolomics and OS follow-up ( n = 18), we built a five-metabolite metabolic risk score (MRS) through bootstrap stability selection and elastic net Cox regression. In paired metabolomic-transcriptomic samples with OS data ( n = 11), we then trained a four-gene RNA surrogate and evaluated agreement in an independent paired validation set ( n = 6), before applying the model to an RNA cohort with OS follow-up ( n = 52). The MRS significantly stratified OS, including in non-transplanted and MRD-positive subsets. Higher MRS was associated with cellular remodeling and KRAS signaling, whereas lower MRS was linked to interferon-related pathways. The RNA surrogate remained independently associated with OS and showed higher discrimination than individual clinical predictors. These findings support metabolite-informed risk stratification in B-ALL and warrant external validation.

The bone marrow microenvironment influences leukemic behavior in B-cell acute lymphoblastic leukemia (B-ALL), but clinically relevant MSC-associated transcriptional signals remain poorly defined. We derived a lineage-specific mesenchymal stromal cell (MSC) signature from a normal bone marrow single-cell RNA-sequencing reference and evaluated it in bulk transcriptomes from 56 newly diagnosed patients with B-ALL. The 35-gene signature formed two co-expression modules. The turquoise module, but not the blue module, was associated with inferior overall survival and distinct exploratory metabolic and immune-related patterns. We then developed a 10-gene-pair model from turquoise-associated genes. The model stratified survival in the derivation cohort and retained prognostic value in the external TARGET cohort using the same cutoff. In multivariable TARGET analysis, the model remained an adverse prognostic factor after adjustment for age, white blood cell count, and day 29 minimal residual disease. Exploratory analyses also identified distinct predicted drug-sensitivity associations. These findings indicate that a single-cell-informed MSC-associated transcriptional signal can support prognostic modeling, while the cellular source of this signal requires orthogonal validation.