Identification of Compounds That Target Acute Myeloid Leukemia Stem Cells Using a Scalable Next-Generation Screening Platform
Bibliographic record
Abstract
Abstract The main barrier to curing acute myeloid leukemia (AML) is disease relapse, which occurs due to therapy resistance and persistence of leukemic stem cells (LSCs) after conventional induction chemotherapy. LSCs possess stem cell properties such as quiescence and self-renewal, as well as a transcriptional signature that resembles that of normal hematopoietic stem and progenitor cells. While patient-derived xenograft (PDX) models are the most stringent way to examine the properties of LSCs in human AML, this labor-intensive assay is not amenable to high throughput approaches to identify druggable vulnerabilities. Although current therapies effectively reduce tumor burden, achieving lasting event-free survival remains a challenge in the management of patients with AML. Current drug discovery efforts are focused on finding agents that eradicate bulk tumor, but not necessarily LSCs. Here, we report a next-generation screening strategy as a novel paradigm that allows the direct elucidation of molecules which antagonize LSC properties. This screening workflow is based on two scalable models of LSCs. The first is the gene expression profile of a core set of 104 genes (LSC104) that are differentially expressed between LSC+ and LSC‒ fractions obtained from primary AML samples and validated in xenotransplantation assays (Ng et al, Nature 2016). This LSC gene expression signature is strongly associated with overall survival and chemotherapy response in independent cohorts of AML patients comprising all subtypes. The second is a continuous AML culture system derived from a patient with relapsed AML that maintains, and allows the prospective enrichment of, a rare population of quiescent, self-renewing LSCs restricted to the CD34+CD38‒ fraction. These LSCs, but not the bulk tumor cells in this system, demonstrate leukemia-initiating capacity both in vitro and in PDX models. Similarly, the LSC fraction of this system, but not the bulk cells, has a LSC104 expression profile that correlates strongly with that of LSC-enriched fractions from patient samples. Together, these components form a clinically-relevant scalable model system to identify compounds with activity against LSC stemness properties, based on alterations in the proportion of LSCs in the continuous AML culture system as well as in the core LSC104 gene expression profile. To this end, we assembled a collection of 1200 curated bioactive small molecules, including 150 metabolic inhibitors and 35 targeted epigenetic probes, and performed a high-dimensional throughput screen using flow-cytometry to examine the effect of these compounds on the LSC-enriched CD34+CD38‒ fraction in our AML culture system. Candidate hits were then tested for their effects on the LSC104 profile. Rigorous analyses unveiled a number of candidate compounds with the potential to antagonize LSC properties, including several already in clinical use or in clinical trials against AML, such as cytarabine, and inhibitors of FLT3, CDK, PLK1, and aurora kinase, as well as several classes of compounds not previously described against AML, targeting NAMPT, BRPF1B, CHK1, and KSP, among others. In conclusion, we describe a next-generation scalable throughput approach that integrates stem cell biology to uncover modulators of stemness in AML. This multi-parametric screening strategy serves as a platform for the deeper understanding of druggable vulnerabilities of LSCs, and serves as a starting point to achieve lasting event-free survival in AML patients. Disclosures Chan: AbbVie: Research Funding; Celgene: Research Funding; Genentech: 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".