Uncovering the Role of CD59 As a Novel Therapeutic Target Against High-Risk Acute Myeloid Leukemia
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a hematologic malignancy driven by acquired genetic mutations in hematopoietic stem and progenitor cells. Continued disease progression leads to the unregulated growth of immature cells of the myeloid lineage, resulting in impaired hematopoiesis, organ failure, and death. High-risk AML is a disease subtype, classified by cytogenetic abnormalities, TP53 mutations, chemotherapy resistance, and dismal overall survival. To uncover the driving factors of poor outcomes in high-risk AML, we performed a multivariable analysis of the BEAT-AML-2.0 patient cohort. We identified that overexpression of the membrane regulatory protein, CD59, serves as a prognostic marker for an increased risk of death. Furthermore, we uncovered that CD59 is overexpressed in high-risk AML patients, including samples with TP53 mutations and refractory disease. CD59 prevents the formation of the membrane attack complex of the complement system and its overexpression is linked with resistance to cancer therapies that activate the complement cascade. However, the intracellular role of CD59 is poorly understood and it is unexplored in AML. Given that the standard of care for AML treatment, which consists of a combination therapy of cytarabine and an anthracycline, does not activate the complement system, we hypothesize that CD59 confers a survival advantage to AML cells in a complement-independent manner. To study the role of CD59 in AML, we constructed doxycycline-inducible short-hairpin RNA (shRNA) expression vectors against CD59 (shCD59) and a non-targeting control (shNT) and transduced them into SKM-1, HL-60, EOL-1, and OCI-M1 AML cell lines. First, we examined the impact of CD59 knockdown on cell growth through a cell competition assay between shCD59 or shNT cells and untransduced cells by culturing them at a 1:1 ratio. We observed that upon the addition of doxycycline, shCD59 cells are outcompeted by untransduced cells whereas the percentage of shNT cells remains unchanged. Cell viability analysis revealed an increased level of cell death in shCD59 cells in comparison to shNT cells. Moreover, we quantified the level of cellular proliferation in shCD59 and shNT cells through 5-ethyl-2'-deoxyuridine (EdU) staining. We observed that shCD59 cells have a signification reduction in cellular proliferation in comparison to shNT cells, highlighting changes in both cellular division and survival pathways upon CD59 knockdown. Importantly, to rule out the impact of alternative complement activation on the observed phenotype, we repeated the previously described experiments in heat-inactivated fetal bovine serum supplement cell culture medium and observed an identical response, confirming a complement-independent role of CD59. Based on our findings, we investigated the mechanism through which CD59 exerts pro-survival signals by performing RNA-sequencing on shCD59 and shNT cells. Through differential expression and pathway analyses, we discovered shCD59 cells exhibit increased inflammatory signalling and apoptotic pathway activation. Conversely, mTOR and MYC signaling pathways are significantly downregulated upon CD59 knockdown, including a reduction in mitotic spindle activity, DNA repair machinery, and DNA replication checkpoints. These results indicate that upon CD59 knockdown, AML cells undergo mitotic catastrophe, driven by deficient cell-cycle checkpoints, DNA damage, and ultimately, cell death. We further examined changes in the signaling landscape of shCD59 and shNT cells by analyzing the cellular phospho-proteome through mass spectrometry. In agreement with our RNA-sequencing analysis, shCD59 cells have increased activation of DNA damage and inflammation markers, such as BCLAF1 and IKBKG, respectively. In contrast, shNT cells display elevated activation of mTOR pathway markers, including EIF4B, and cell cycle drivers, such as CDC20. In summary, we present CD59 overexpression as a driver of AML progression and poor clinical outcomes. Through functional and computational studies, we have shown CD59 expression is critical for cell growth and cell survival, and as such, identifying it as a novel therapeutic target. The survival outlook for high-risk AML patients has remained unchanged for the past 25 years. Our findings offer valuable insights that can significantly alter the current treatment landscape of AML.
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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) | 0.000 | 0.000 |
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".