Engraftment Patterns in NOD.SCID Mice Predict Outcome in Human AML
Bibliographic record
Abstract
Abstract The NOD.SCID xenotransplantation assay is a key model system for interrogating the biology of leukemic stem cells (LSCs) in human acute myeloid leukemia (AML). Approximately 50% of AMLs can generate human grafts in immunodeficient mice that recapitulate the features of the parent sample. However, some AML samples generate non-leukemic grafts upon xenotransplantation. We recently reported that multilineage (ML) grafts, comprised of both B-lymphoid and myeloid cells, are generated by preleukemic hematopoietic stem cells (preL-HSCs; Shlush et al. Nature 2014). PreL-HSCs contain a subset of the mutations present in leukemic blasts and have a competitive growth advantage over wildtype HSCs leading to clonal expansion in vivo, yet retain multilineage differentiation capacity. To investigate the relationship between patient outcomes and the biological properties of LSCs and preL-HSCs as reflected by different engraftment patterns, we transplanted 272 diagnostic patient samples, representing a broad cross-section of adult AML, into sublethally irradiated NOD.SCID mice by intrafemoral injection. Human chimerism was assessed 8-10 weeks post-transplant by flow cytometry. 41% of samples generated AML xenografts, defined as a human graft containing >90% myeloid (CD33+CD19-CD45+) cells. Three patterns of engraftment were seen with the remaining samples: no human graft, defined as <0.5% CD45+ cells (27%), isolated CD3+CD45+ T-cell (TC) grafts (10%), and ML human grafts composed of CD33+CD45+ myeloid cells plus >10% CD19+CD33-CD45+ B-cells (22%). Among patients whose samples generated ML grafts compared to AML grafts or TC/no graft, secondary AML was less common (10% vs. 27% vs. 26%, respectively; P=0.03). Associations between engraftment pattern and other baseline clinical characteristics, including age, white blood cell (WBC) count and cytogenetics, did not reach statistical significance in this cohort. However, there was a strong correlation between AML engraftment capacity and response to standard induction chemotherapy. AML engrafters had lower complete remission (CR) rates compared to all other patients as a group (51% vs. 81%; P<0.0001), and significantly decreased overall survival (OS; median 10.6 vs. 28.7 mos; P<0.0001). In a multivariate analysis of OS, AML engraftment capacity retained prognostic significance (hazard ratio (HR) 2.54; P<0.0001), along with established factors such as adverse cytogenetics (HR= 3.71; P<0.0001) and WBC count (HR= 1.005; P=0.001). The level of human leukemic engraftment achieved in mice also correlated with patient survival. In a median split analysis, OS was worse in patients whose cells generated AML grafts with high (>16.6%) vs. low (≤16.6%) levels of human chimerism (median OS of 8.2 vs. 14.5 mos; P=0.001). The ability of a diagnostic AML sample to generate ML xenografts likely reflects a high frequency of preL-HSCs, postulated to be a source for relapse. Indeed, despite comparable CR rates in patients whose samples generated ML vs. TC/no graft (85% vs. 77%; P=0.28), the relapse-free survival of ML engrafting patients was shorter (median of 19.3 vs. 50.2 months; P=0.04). The cumulative incidence time-to-relapse (TTR) of ML engrafters (median TTR 19.7 mos) was also less than that of patients whose samples generated TC/no graft (median TTR not reached; P=0.01) and comparable to that of AML engrafters (9.6 mos; P=0.11). Thus, the presence of significant numbers of preL-HSCs in diagnostic AML samples, as evidenced by the generation of ML xenografts, is associated with earlier relapse, consistent with our prior finding that preL-HSCs persist in remission and thus may serve as a reservoir for clonal evolution. Interestingly, despite the shorter time-to-relapse, the OS of patients whose samples generated ML grafts was comparable to those who generated TC/no grafts (median 30.4 vs. 27.1 mos, P=0.95), and significantly longer than those who generated AML grafts (10.6 mos; P<0.0001), suggesting that relapsed disease arising from preL-HSCs behaves similarly to de novo disease and responds well to re-induction. Thus, the NOD.SCID xenotransplantation model captures functional properties of LSCs and preL-HSCs that are clinically relevant, validating its use to study the biology of these disease-sustaining cell populations and evaluate novel AML therapies. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 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) | 0.001 | 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".