Henri Colyn Bwanika

Acute lymphoblastic leukemia (ALL) exhibits molecular and metabolic heterogeneity that limits therapeutic efficacy. Although antimetabolites remain central to ALL treatment, determinants of therapeutic sensitivity and resistance remain incompletely understood. Here, we investigated the activity and molecular response landscape of the purine analog triciribine (TCN) across diverse ALL models using integrated phosphoproteomics, thermal proteome profiling, and quantitative proteomics. TCN showed potent cytotoxicity across ALL cell lines and a drug response profile distinct from canonical PI3K–Akt–mTOR inhibitors. Early phosphoproteomics revealed no consistent suppression of Akt pathway signaling, with transient activation of several Akt-associated readouts and inhibition of cyclin-dependent kinase signaling. The monophosphorylated metabolite TCN-P was the predominant intracellular active species, and adenosine kinase (ADK) was required for TCN-P generation and cytotoxicity. Proteome Integral Solubility Alteration profiling identified candidate TCN-P interactions involving nucleotide metabolism, DNA replication, and protein synthesis, associated with impaired purine biosynthesis, DNA damage, translational stress, and cell-cycle perturbation. Across ALL cell lines and primary samples, ADK levels correlated positively with TCN sensitivity, supporting ADK-dependent metabolism as a major determinant of therapeutic response. Together, these findings identify TCN as a metabolically activated antileukemic agent with broad antimetabolite activity and nominate ADK as a potential predictive biomarker for precision therapy in ALL.

Detailed description of methods.

Sensitivity of all samples as sensitive or resistant to oxaliplatin and SN-38 using median GR50 and median AOC.

Classification of the eight fastest growing samples as either sensitive or resistant to oxaliplatin and SN-38 at day 7 of the experiment according to either median ED20 or median GR50.

Growth rate (relative total area) of negative controls in all growing samples.

Classification of Samples 1, 3, 7, 8, 10, 12, and 13 as either sensitive or resistant to oxaliplatin and SN-38 for the total duration of the experiment, according to the median GR50 of all 16 samples on day 7 of the experiment.

A and B, Point estimates of sample library AOC. The dotted red line represents median AOC, and the blue area represents the IQR. Dot color indicates sample sensitivity according to the median, with red indicating resistant and blue indicating sensitive. C and D, Correlation plots of GR50 and AOC. The y-axis is log-transformed, and the x-axis is linear. The dotted blue line indicates the median GR50, the dotted red line indicates the median AOC, and the whole blue line indicates linear regression of log2(GR50) and AOC with CIs. GR50 is in μmol/L for oxaliplatin (left) and nmol/L for SN-38 (right).

Dose–response curves for SN-38 generated using the log-logistic 3-parametric model fitted to either relative total area (A and B) or GR metrics (C and D). The x-axis displays drug concentration in nanomolar (nmol/L), and the y-axis displays inferred response (normalized relative total area/GR metric). Solid black line indicates the library mean inferred response across both fast- and slow-growing samples per readout, and dashed red line indicates the mean inferred response for only the fast- or slow-growing samples, respectively, per readout. Vertical red lines indicate median ED values for increasing levels of inhibition (20%, 30%, 40%, and 50%) across both fast- and slow-growing samples. A, Dose–response curves of the eight slowest-growing samples fitted to relative total area. B, Dose–response curves of the eight fastest-growing samples fitted to relative total area. C, Dose–response curves of the eight slowest-growing samples fitted to GR metrics. D, Dose–response curves of the eight fastest-growing samples fitted to GR metrics.

Barplot of estimated ED20 of simulated samples.