Improved Relapse Prediction in Pediatric Acute Myeloid Leukemia By Deconvolving Lineage-Specific and Cancer-Specific Features in Single-Cell Data

Introduction

While most children with acute myeloid leukemia (AML) achieve first remission, nearly 40% will relapse. Of these children, few survive to a second remission even with highly-escalated treatment protocols. Recent studies have shown that many AML patients harbor rare, stem cell-like subpopulations that resist chemotherapy and drive relapse. However, the exact characteristics of these relapse-associated cells are a matter of contention, with reported phenotypes spanning the hematopoietic developmental continuum. In some patients, treatment-resistant cells can be detected as minimal residual disease (MRD), which is often used to predict relapse, albeit with limited accuracy and only after induction chemotherapy. Thus, the identity of treatment-resistant cells as well as their relationship to normal progenitors remain mysterious, thereby limiting the development of targeted therapies for pediatric AML.

Here, we present a computational approach for decomposing high-dimensional single-cell measurements into two components: a lineage-specific component that can be used to align cancer cells with specific stages of myeloid development and a cancer-specific component that can be used to identify aberrant phenotypes unique to AML cells. We show that, together, these components can be used at the time of diagnosis to predict relapse more accurately than clinical information alone.

Methods and Results

Using mass cytometry, we analyzed paired diagnostic and post-induction samples collected from 19 (8 relapse, 11 non-relapse) pediatric patients who enrolled on the Children's Oncology Group trial AAML1031. All patients were treated on the control arm and consented to banking of tissue for research. We also included 5 bone marrow samples from healthy donors. After thawing, samples were divided in half and stimulated with conditioned medium from the human bone marrow stromal cell line HS-5 to activate relevant signaling pathways, or left unstimulated. An average of 5 x 10⁵ cells per patient were analyzed for each condition. The mass cytometry panel included 31 antibodies to surface markers, 6 antibodies to intracellular signaling mediators, and 4 antibodies to intracellular proteins and transcription factors.

Following data collection, the singular value decomposition was applied to the data matrix of healthy single-cell measurements to construct a linear subspace representing the predominant protein expression programs ("eigencells") within the healthy myeloid developmental continuum. By projecting AML single-cell measurements onto this subspace, we derived a healthy feature vector aligned with the healthy subspace and a cancer-specific feature vector orthogonal to the healthy subspace for each AML cell. These feature vectors-along with clinical metadata about each patient including age, blast percentage at diagnosis, and cytogenetic status-were used as the input to regularized Cox proportional hazards models predicting time-to-relapse for each patient. Using the relative risk scores from the proportional hazards model, patients were assigned to high-risk or low-risk groups according to the optimal log-rank test threshold.

The baseline clinical model used only age, blast percentage at diagnosis, and cytogenetic status as predictors and predicted relapse status with an accuracy of 13/19 (68%). This baseline model was outperformed by the model constructed using the average value of the single-cell cancer-specific feature vectors for each patient, which predicted relapse status with an accuracy of 16/19 (84%). Interestingly, despite using only information available at diagnosis, the single-cell model also outperformed a clinical model incorporating patients' MRD status after induction chemotherapy, which predicted relapse with an accuracy of 15/19 (79%). Interrogation of the coefficients of the single-cell feature model revealed specific cellular signaling programs associated with relapse, including enhanced pCreb and pSTAT1 signaling as well as depleted pSTAT5 signaling relative to healthy lineage cells (Figure 1).

Conclusions

These results support the feasibility of predicting relapse in AML as early as diagnosis by leveraging a computational approach that compares cancer cells to the native lineage from which they arise. Validation of this approach in an independent cohort is ongoing and will be presented.

No relevant conflicts of interest to declare.