A Transcriptional Classifier Identifies Pediatric T-Cell Acute Lymphoblastic Leukemias at High Risk for End of Induction Minimal Residual Disease

Upfront risk stratification with incorporation of risk-adapted therapy continues to be a pressing need for pediatric T-ALL. However, robust molecular or cytogenetic biomarkers are currently lacking. Instead, initial response to therapy, measured as end of induction minimal residual disease (MRD), is the only prognostic factor used in most cooperative groups. This inability to stratify patients at diagnosis limits the early introduction of rationally-designed therapies that may better achieve long-term remissions. To address this, we asked whether gene expression profiling performed at diagnosis may identify patients at high risk for future MRD+, enabling earlier therapeutic intervention. To test this, we used a training dataset of RNA-seq data from diagnostic samples from patients enrolled on the Children's Oncology Group (COG) AALL0434 trial. Using leave-one-out cross-validation, we defined a risk score (RS) for MRD+ based on the expression of 119 genes. This RS indicates the probability of being MRD+ for a given gene expression pattern. Gene ontology analysis of the genes in our model demonstrated enrichment for genes involved in signal transduction, lymphocyte differentiation and activation, and cell death. Using this model, we found that early T-cell precursor (ETP) samples (n=19), of which 79% were MRD+, had uniformly high RS, with an average of 78 (SD=17). In contrast, non-ETP samples (n=146), of which 26% were MRD+, had an average RS of 26 (SD=21), with MRD+ patients having a significantly higher RS relative to MRD- patients (p<0.0001). When we analyzed the expression pattern of the 119 genes, we found that ETP samples had a relatively uniform pattern, consistent with their high rate of MRD+. Non-ETP samples with RS<50 had a pattern that was inverted relative to the ETP samples, while those with RS>50 closely mirrored the ETP samples. Using a RS cutoff of 50 to identify patients at high risk for MRD+ gives a 97% specificity in this training cohort. Given the similar gene expression pattern between ETPs and RS>50 non-ETPs, we hypothesized that this RS classification may relate to differences in developmental stage. We found that ETP and RS>50 non-ETP samples were significantly enriched for the LMO2/LYL1, HOXA, and TLX3 subgroups of T-ALL, with the subgroup distribution in the RS>50 non-ETP samples differing significantly from the RS<50 non-ETP samples (p=0.001). Furthermore, we found that the RS>50 non-ETP samples, relative to the RS<50 samples, were enriched for lacking a TCRb rearrangement (p<0.0001), indicative of an earlier developmental stage. To determine the generalizability of our gene expression classifier, we applied our fixed model to an independent validation cohort of 826 non-ETP samples from the same COG AALL0434 trial. Here, we again found that RS>50 non-ETP samples (n=130) were significantly enriched for MRD+ relative to RS<50 samples (n=696; p<0.0001). Next, we applied our classifier model to a pilot cohort of samples from patients enrolled on COG AALL1231, which used dexamethasone rather than prednisone as the induction steroid. In this group of 31 diagnostic non-ETP samples, RS>50 samples were similarly enriched for MRD+ (p=0.05), suggesting that this classifier may retain its prognostic value across multiple induction steroids. Finally, to facilitate clinical implementation of this prognostic tool, we transitioned to the NanoString nCounter platform, a more rapid and cost-effective means of performing targeted gene expression analysis. Using 364 samples from COG AALL0434 for which we had corresponding RNA-seq data, we assessed the performance of our custom NanoString assay and found a strong correlation between the RS calculated using each of the two platforms (concordance correlation coefficient = 0.98), suggesting that the prognostic value of the classifier is retained on the NanoString assay. In conclusion, while ETP samples are commonly MRD+ and are readily identifiable by their unique cell surface immunophenotype, non-ETP samples lack a surface immunophenotype that correlates with MRD+. Here, we present a targeted gene expression classifier, validated on a clinically-tractable platform, that identifies at diagnosis a subset of non-ETP T-ALLs with a gene expression pattern that resembles that of ETP T-ALLs and that are at high risk for future MRD+.

Raetz:BMS: Other: Data and Safety Monitoring Board; Pfizer: Research Funding. Hunger:Servier: Honoraria; Jazz: Honoraria; Amgen: Current equity holder in private company, Honoraria. Hermiston:Novartis: Membership on an entity's Board of Directors or advisory committees; Sobi: Membership on an entity's Board of Directors or advisory committees. Yang:Takeda: Research Funding. Mullighan:Abbvie: Research Funding; Pfizer: Research Funding; Amgen: Honoraria; Illumina: Honoraria; Consulting: Honoraria; BEAM: Honoraria; FAZE: Honoraria. Teachey:Sobi: Consultancy; BEAM Therapeutics: Consultancy. Olshen:HTG Molecular Diagnostics: Consultancy.