Broad Activity of Apto-253 in AML and Other Hematologic Malignancies Correlates with KLF4 Expression Level
Bibliographic record
Abstract
Abstract Introduction: Aberrant expression of the homeodomain transcription factor CDX2 has recently been reported in a large proportion of AML cases. One consequence of CDX2 deregulation appears to be repressed expression of the transcription factor KLF4. Repression of KLF4 was shown to be critical for CDX2-mediated tumorigenesis, and forced genetic de-repression of KLF4 led to apoptosis of AML cells. APTO-253 is a novel small molecule that induces the expression of KLF4 and is cytotoxic to AML cell lines at low-nanomolar concentrations. We evaluated the activity of APTO-253 against a broad panel of primary specimens from patients with acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and myelodysplastic syndromes/myeloproliferative neoplasms (MDS/MPN). APTO-253 was tested both as a single agent and in combinations with 2 other emerging targeted therapies, the BET bromodomain inhibitor JQ1 and the FLT3 inhibitor quizartinib. Methods: We used an ex vivo drug sensitivity assay to determine the activity of APTO-253, JQ1, and quizartinib across increasing concentrations of each agent up to 10 μM. Combinations were tested at fixed, equimolar ratios over the same concentration range. After a 3-day ex vivo culture, cell viability was assessed using a colorimetric tetrazolium-based MTS assay, and IC50 values were calculated. RNA-Seq was performed on AML specimens to permit investigation of correlations of drug sensitivity with gene expression levels. Results: We evaluated specimens from 177 patients with a variety of hematologic malignancy diagnoses (80 AML, 72 CLL, 25 MDS/MPN). The highest frequency of APTO-253 sensitivity occurred in AML, with 43/80 (54%) samples exhibiting an IC50 <1 μM. At this cutoff, 25/72 (35%) CLL samples and 3/25 (12%) MDS/MPN samples were sensitive to APTO-253. The average expression of KLF4 mRNA was 2-fold lower among AML samples with an IC50 <1 µM compared to those with IC50 >1 µM (p=0.07). Approximately 65% (56/87) of cases tested with a combination of APTO-253 and JQ1 showed the combination IC50 to be at least 2-fold lower than the IC50 of either agent alone. This enhanced efficacy of APTO-253 with JQ1 was observed across all 3 hematologic malignancies tested, whereas quizartinib enhancement of APTO-253 sensitivity was confined to AML (14/38, or 37% showed reduced IC50). Conclusions: These results support the potential of KLF4 as an important and frequently dysregulated master transcription factor in AML and suggest that the KLF4 inducer APTO-253 is effective at killing tumor cells in a majority of AML samples. The data also indicate activity of APTO-253 in other hematologic malignancies, namely CLL. Expression level of KLF4 may be one component of a biomarker for prediction of APTO-253 efficacy; a more extensive global gene expression signature analysis is under way. Finally, these data have identified prominent interaction of APTO-253 with the BET bromodomain inhibitor JQ1, as well as AML-restricted interaction of APTO-253 with the FLT3 inhibitor quizartinib, suggesting these classes of drugs as potential combination partners for APTO-253. Disclosures Rice: Aptose Biosciences: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Howell:Aptose Biosciences: Consultancy, Equity Ownership; Angstrom: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Abeoda: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; InhibRx: Equity Ownership. Vellanki:Aptose Biosciences: Employment, Equity Ownership. Druker:Oncotide Pharmaceuticals: Research Funding; Sage Bionetworks: Research Funding; Fred Hutchinson Cancer Research Center: Research Funding; Bristol-Myers Squibb: Research Funding; Novartis Pharmaceuticals: Research Funding; Henry Stewart Talks: Patents & Royalties; McGraw Hill: Patents & Royalties; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; ARIAD: Research Funding; AstraZeneca: Consultancy; Aptose Therapeutics, Inc (formerly Lorus): Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; CTI Biosciences: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Millipore: Patents & Royalties; Roche TCRC, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cylene Pharmaceuticals: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Tyner:Incyte: Research Funding; Janssen Pharmaceuticals: Research Funding; Constellation Pharmaceuticals: Research Funding; Array Biopharma: Research Funding; Aptose Biosciences: Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".