Uncovering resistance before it starts
"FLT3 inhibitors have improved outcomes, but resistance still emerges far too often. We wanted to understand what AML cells look like before they become resistant—what early states might prime them to survive treatment.”, says Dr. Johanna Eriksson, first author of the study.
Using ReSisTrace, a cutting-edge single-cell lineage tracing technique, the team tracked thousands of uniquely barcoded AML cells through treatment with midostaurin or quizartinib. By pairing pre-treatment single-cell transcriptomes with lineage outcomes, they identified pre-resistant cell states—cells that were already transcriptionally primed to survive before drug exposure. “This study provides a framework to predict resistance—before it emerges—and guide the design of rational combination therapies to prevent it.” Eriksson continues.
Computational model reveals druggable vulnerabilities
PhD researcher Shuyu Zheng, who led the computational analyses, explains the methodological impact:
“ReSisTrace generates extremely high-resolution lineage and transcriptomic data. Our challenge was to distinguish true pre-resistance signals from growth-related changes and technical noise. By integrating differential expression, bootstrapping, and connectivity map analysis, we could pinpoint genes and pathways consistently enriched in cells destined to resist therapy.”
One notable target gene was GSPT1, which was upregulated in pre-resistant cells across both FLT3 inhibitors. Targeting GSPT1 with its selective degrader CC-90009 produced robust synergy with midostaurin and quizartinib in cell lines and primary AML samples. These findings were further validated in an AML patient-derived xenograft mouse model, in which the combination significantly reduced tumour burden and improved survival compared with single agents.
Systems-level insights into AML plasticity
Collaborator Dr. Anna Vähärautio highlights the broader biological implications: “This study strengthens the notion that resistance is not always driven by a genetic event—it can be encoded in the cell’s transcriptional state even before the treatment begins. By mapping these states, we begin to understand how non-genetic priming drives drug tolerance in various cancers, including AML.”
In addition to GSPT1, the study identified multiple compounds predicted to shift cells back into pre-sensitive states, including linsitinib, vistusertib, and meisoindigo—each targeting signalling pathways parallel or downstream of FLT3. These drugs exhibited strong synergy with FLT3 inhibitors in both AML cell lines and primary patient samples.
A path toward more durable FLT3 inhibitor responses and precision medicine
By combining single-cell lineage tracing, computational modelling, and experimental validation, the team demonstrates how treatment-resistant states can be detected and therapeutically targeted at their origin.
Last author and lead investigator Prof. Jing Tang emphasizes the translational potential: “Our study opens a new avenue for personalizing treatment strategies based on early cellular states rather than waiting for relapse. We hope that these insights will lead to more durable remissions for patients with FLT3-ITD-positive AML.”
Tang concludes: “Our approach not only identifies actionable vulnerabilities in AML but also sets a precedent for studying resistance across many cancers where early cellular plasticity drives relapse.”