Whole-transcriptome analysis in acute lymphoblastic leukemia: a report from the DFCI ALL Consortium Protocol 16-001

Abstract

The molecular hallmark of childhood acute lymphoblastic leukemia (ALL) is characterized by recurrent, prognostic genetic alterations, many of which are cryptic by conventional cytogenetics. RNA sequencing (RNA-seq) is a powerful next-generation sequencing technology that can simultaneously identify cryptic gene rearrangements, sequence mutations and gene expression profiles in a single assay. We examined the feasibility and utility of incorporating RNA-seq into a prospective multicenter phase 3 clinical trial for children with newly diagnosed ALL. The Dana-Farber Cancer Institute ALL Consortium Protocol 16-001 enrolled 173 patients with ALL who consented to optional studies and had samples available for RNA-seq. RNA-seq identified at least 1 alteration in 157 patients (91%). Fusion detection was 100% concordant with results obtained from conventional cytogenetic analyses. An additional 56 gene fusions were identified by RNA-seq, many of which confer prognostic or therapeutic significance. Gene expression profiling enabled further molecular classification into the following B-cell ALL (B-ALL) subgroups: high hyperdiploid (n = 36), ETV6-RUNX1/-like (n = 31), TCF3-PBX1 (n = 7), KMT2A-rearranged (KMT2A-R; n = 5), intrachromosomal amplification of chromosome 21 (iAMP21) (n = 1), hypodiploid (n = 1), Philadelphia chromosome (Ph)-positive/Ph-like (n = 16), DUX4-R (n = 11), PAX5 alterations (PAX5 alt; n = 11), PAX5 P80R (n = 1), ZNF384-R (n = 4), NUTM1-R (n = 1), MEF2D-R (n = 1), and others (n = 10). RNA-seq identified 141 nonsynonymous mutations in 93 patients (54%); the most frequent were RAS-MAPK pathway mutations. Among 79 patients with both low-density array and RNA-seq data for the Philadelphia chromosome-like gene signature prediction, results were concordant in 74 patients (94%). In conclusion, RNA-seq identified several clinically relevant genetic alterations not detected by conventional methods, which supports the integration of this technology into front-line pediatric ALL trials. This trial was registered at www.clinicaltrials.gov as #NCT03020030.

© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.

Figures

Graphical abstract

Figure 1.

Comprehensive heatmap of molecular profiling findings by RNA-seq of 173 patients with ALL enrolled on DFCI 16-001. RNA-seq data from 173 patients with ALL are summarized in this heatmap. Patients were classified on the basis of their respective clustering analysis subtype, gene fusions, and somatic mutations. Specific gene fusions and mutational categories are indicated by colored squares and classified by associated gene signaling pathways. Mutation types included single nucleotide variants, indels, and intragenic IKZF1 deletion (IK6). iAMP21, intrachromosomal amplification of chromosome 21; NA, not applicable; TCR-R, T cell receptor rearrangement; VUS, variant of unknown significance.

Figure 2.

Timeline of the clinical implementation of RNA-seq from sample receipt to report delivery and factors contributing to timeline variation at each step.

Figure 3.

Novel ZBTB44-JAK2 fusion detected in a patient with Ph-like ALL. Exon 2 of ZBTB44 fused in-frame to exon 19 of JAK2, conserving an intact kinase domain. The fusion was validated by reverse transcriptase polymerase chain reaction and Sanger sequencing. The figure was adapted from Arriba output.

Figure 4.

Detection of intragenic IKZF1 deletions by RNA-seq. Intragenic deletions of exons 4 to 7 of IKZF1 visualized using the GVIZ R package. Green line corresponds to normal IKZF1 transcript (IK1), and the red line corresponds to IK6 transcript missing exons 4 to 7 (example with patient #22).

Figure 5.

Molecular subtype clustering of patients with ALL based on gene expression signatures. (A) Hierarchical clustering and (B) experimental t-distributed stochastic neighbor embedding (tSNE) performed using the top 500 variable genes from 1134 ALL samples from both in-house and public RNA-seq data sets. (C) The neural network probability score for each of the subtypes listed for panels A-C. Results for patient #124 are shown by the arrow in A and the red dot in B.

Figure 6.

Estimation of blood cell populations using a deconvolution tool. Proportion of primary hematopoietic cell types in 173 patients with ALL using 13 primary cell-type expression profiles. CLP, common lymphoid progenitor; CMP, common myeloid progenitor; GMP, granulocyte-monocyte progenitor; HSC, hematopoietic stem cell; LMPP, lymphoid-primed multipotential progenitor; MEP, megakaryocyte-erythrocyte progenitor; MPP, multipotent progenitor; NK cell, natural killer cell.

Figure 7.

Proposed tiered algorithm for a time- and cost-effective clinical implementation of RNA-seq. Proposed algorithm incorporating FISH and RNA-seq with an estimated turnaround time of 4 weeks from the time of sample receipt to final report delivery based on our experience. BA FISH, break-apart fluorescence in situ hybridization.