Genomic Biomarkers of Poor Outcome in Patients with T-Cell Lymphoblastic Lymphoma

Introduction: T-cell lymphoblastic lymphoma (T-LL) is the second most common subtype of non-Hodgkin lymphoma in children. T-LL mirrors T-cell acute lymphoblastic leukemia (T-ALL) in morphology and immunophenotype. Historically, T-ALL and T-LL were considered a spectrum of one disease; as such, treatment regimens have harmonized over time. However, recent Children’s Oncology Group (COG) phase 3 trials demonstrated different outcomes for T-LL and T-ALL when treated with identical investigational agents. For example, outcomes for T-LL were significantly improved with the addition of bortezomib, with no benefit for T-ALL. Identification of informative genomic biomarkers may give insight into biologic differences between these two entities. Sequencing from formalin fixed T-LL samples is now possible, providing robust DNA/RNA that can be compared with T-ALL samples. Herein we present comprehensive transcriptional analysis of T-LL in one of the largest cohorts of patients sequenced to date.

Methods: RNA was derived from formalin fixed, paraffin-embedded tissue sections of 203 T-LL patients (ages 1-29 years) treated on COG AALL0434 (NCT00408005) and AALL1231(NCT02112916). Healthy bone marrow and thymi samples were used as controls (n=6). Material was scraped from 1-2 slides per sample. A nuclease protection assay (HTG EdgeSeq Transcriptome panel kit) with 19398 gene-specific probes was conducted followed by barcoded cDNA and sequencing library generation. FASTQ files were demultiplexed and parsed using HTG Reveal software. HTG sequencing has been validated to highly correlate with RNA-sequencing data (PMID 35712667). Differential expression analysis between patients who experienced an event (induction failure/relapse/death) versus patients without event was conducted using DESeq2 (PMID 25516281). A log2 fold change cut-off of 2 and adjusted P-value threshold of 0.05 (corrected using the Benjamini and Hochberg method) was implemented.

Results: Comprehensive transcriptome profiling was successfully obtained from 166/203 (81.8%) samples. We observed that Epigenetic, Ribosome, and Transcriptional regulation pathway-associated genes were overexpressed. LEF1, RPL3, B2M, and HISTH1H2AE were amongst the highest expressed genes. JAK2 and STAT5B were overall underexpressed, and NOTCH1 was relatively overexpressed among patients in comparison to control samples (P<0.001). Downregulation of CDKN2A was also common (n=85, 51%). We compared gene expression profiles between individuals who had events (n=17: 12% induction failure, 65% progression/relapse, 24% death) to individuals without events (n=149). We identified 8 genes with a log2 fold change > 2 and adjusted P<0.05 between event/no-event cohorts. Specifically, NRARP was overexpressed among patients with events, whereas SPINK2, C1QTNF4, SERHL2, BAALC, and homeobox genes (HOXA9, HOXA7, and HOXA3) were significantly under-expressed. We also compared gene expression between patients with (>=1%) and without (<1%) bone marrow flow-based minimal disseminated disease (MDD) at diagnosis and identified enhanced expression of 20 genes, including MYCN, FLT3, TCTEX1D1, and HOXA13 in MDD+ patients as compared with MDD- patients.

Discussion: Herein we report significant differences in gene expression between T-LL patients who experienced refractory disease/relapse/death as compared to patients without these events. Notably, NRARP was upregulated in T-LL patients with events. In T-ALL, NRARP has been shown to block NOTCH1 signaling, promote proliferation of T-ALL blasts and potentiate Wnt pathway signaling (PMID 31586130). We observed lower HOXA gene expression among T-LL patients experiencing refractory disease/relapse/death – potentially suggesting homeobox alterations may be more common in the lower-risk T-LL cohort. We identified differential gene expression between MDD+ and MDD- patients; there may be alternative pathways of lymphomagenesis between these cohorts. Whole genome sequencing (WGS) is completed on 174 cases and analysis is underway. Integrated analysis of WGS with gene expression may further elucidate mechanisms driving differential deregulation and prognostic associations in T-LL, as well as provide opportunity for comparison with T-ALL. HN/LU/RS and DTT/RM/MH contributed equally.