A stemness screen reveals C3orf54/INKA1 as a promoter of human leukemia stem cell latency
Bibliographic record
Abstract
Abstract There is a growing body of evidence that the molecular properties of leukemia stem cells (LSCs) are associated with clinical outcomes in acute myeloid leukemia (AML), and LSCs have been linked to therapy failure and relapse. Thus, a better understanding of the molecular mechanisms that contribute to the persistence and regenerative potential of LSCs is expected to result in the development of more effective therapies. We therefore interrogated functionally validated data sets of LSC-specific genes together with their known protein interactors and selected 64 candidates for a competitive in vivo gain-of-function screen to identify genes that enhanced stemness in human cord blood hematopoietic stem and progenitor cells. A consistent effect observed for the top hits was the ability to restrain early repopulation kinetics while preserving regenerative potential. Overexpression (OE) of the most promising candidate, the orphan gene C3orf54/INKA1, in a patient-derived AML model (8227) promoted the retention of LSCs in a primitive state manifested by relative expansion of CD34+ cells, accumulation of cells in G0, and reduced output of differentiated progeny. Despite delayed early repopulation, at later times, INKA1-OE resulted in the expansion of self-renewing LSCs. In contrast, INKA1 silencing in primary AML reduced regenerative potential. Mechanistically, our multidimensional confocal analysis found that INKA1 regulates G0 exit by interfering with nuclear localization of its target PAK4, with concomitant reduction of global H4K16ac levels. These data identify INKA1 as a novel regulator of LSC latency and reveal a link between the regulation of stem cell kinetics and pool size during regeneration.
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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".