Dubermatinib

Older patients who have acute myeloid leukemia (AML) with mutant TP53 and/or complex karyotype have a dismal prognosis and lack treatment options. Having previously demonstrated that TP-0903, a multikinase inhibitor, has compelling preclinical activity in drug-resistant AML, including TP53-mutated AML, we evaluated the clinical activity of TP-0903 in combination with decitabine.

This was a multicenter, open-label, phase 1b/2 substudy of Beat AML Master Trial (ClinicalTrials.gov: NCT03013998). The phase 1b portion used a 3 + 3 design, and the phase 2 portion used a Simon two-stage design. Eligible patients ages ≥60 years who had newly diagnosed AML with mutations in TP53 and/or complex karyotype (≥3 cytogenetic abnormalities) received either 37 mg (group 1) or 25 mg (group 2) TP-0903 orally on days 1 to 21 with decitabine 20 mg/m2 on days 1 to 10 for up to three 28-day induction cycles, followed by up to 30 maintenance cycles in which decitabine dosing was reduced to days 1 to 5. The primary endpoint was complete remission (CR) by the end of six cycles of treatment.

The overall composite remission rate (CR/CR with incomplete count recovery/CR with hematologic improvement) was 33.3% in group 1 and 50.0% in group 2, with CR rates of 13.3% and 25%, respectively. The median overall survival for groups 1 and 2 was 7.6 and 7.5 months, respectively.

The combination of TP-0903 and decitabine was reasonably tolerated and had activity in this patient population. Further research and novel treatment strategies are necessary to improve outcomes for patients with these high-risk subtypes of AML.

Treatment options for AML with mutant TP53 and/or complex karyotype are limited. In a phase 1b/2 clinical trial, TP-0903, a multikinase inhibitor, was well-tolerated and had activity comparable with other emerging therapies. Our results suggest that TP-0903 may offer insight and serve as a benchmark for the development of future agents leveraging similar strategies or scaffolds to improve outcomes in these intractable subtypes of AML.

Therapy resistance in breast cancer is increasingly attributed to polyploid giant cancer cells (PGCCs), which arise through whole genome doubling and exhibit heightened resilience to standard treatments. Characterized by enlarged nuclei and increased DNA content, these cells tend to be dormant under therapeutic stress, driving disease relapse. Despite their critical role in resistance, strategies to effectively target PGCCs are limited, largely due to the lack of high-throughput methods for assessing their viability. Traditional assays lack the sensitivity needed to detect PGCC-specific elimination, prompting the development of novel approaches. To address this challenge, we developed a high-throughput single-cell morphological analysis workflow designed to differentiate compounds that selectively inhibit non-PGCCs, PGCCs, or both. Using this method, we screened a library of 2726 FDA Phase 1-approved drugs, identifying promising anti-PGCC candidates, including proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones. Notably, RNA-Seq analysis of cells treated with the macrocyclic lactone Pyronaridine revealed AXL inhibition as a potential strategy for targeting PGCCs. Although our single-cell morphological analysis pipeline is powerful, empirical testing of all existing compounds is impractical and inefficient. To overcome this limitation, we trained a machine learning model to predict anti-PGCC efficacy in silico, integrating chemical fingerprints and compound descriptions from prior publications and databases. The model demonstrated a high correlation with experimental outcomes and predicted efficacious compounds in an expanded library of over 6,000 drugs. Among the top-ranked predictions, we experimentally validated five compounds as potent PGCC inhibitors using cell lines and patient-derived models. These findings underscore the synergistic potential of integrating high-throughput empirical screening with machine learning-based virtual screening to accelerate the discovery of novel therapies, particularly for targeting therapy-resistant PGCCs in breast cancer.