Identification and targeting of CD22ΔE12 as a molecular RNAi target to overcome drug resistance in high-risk B-lineage leukemias and lymphomas

Abstract

AimCD22ΔE12 as an oncogenic driver lesion in aggressive and drug-resistant B-precursor acute lymphoblastic leukemia (BPL) cells. The purpose of the present study was to identify the CD22ΔE12-specific signature transcriptome in human BPL cells and evaluate the clinical potential of a nanoscale formulation of CD22ΔE12-siRNA as an RNAi therapeutic against drug-resistant BPL. CD22ΔE12-siRNA nanoparticles significantly improved the event-free survival (EFS) outcome of NOD/SCID (NS) mice challenged with human BPL xenograft cells.MethodsGene expression and translational bioinformatics methods were applied to examine the expression of the CD22ΔE12-specific signature transcriptome in human BPL cells in subsets of BPL patients. Survival analysis for mice challenged with BPL cells and treated with CD22ΔE12 siRNA was performed using standard methods.ResultsLeukemia cells from CD22ΔE12-Tg mice exhibit gene and protein expression profiles consistent with constitutive activation of multiple signaling networks, mimicking the profiles of relapsed BPL patients as well as newly diagnosed high-risk patients with BCR-ABL+/Philadelphia chromosome (Ph)+ BPL as well as Ph-like BPL. A nanoscale formulation of CD22ΔE12-siRNA abrogated the in vivo clonogenicity of the leukemia-initiating leukemic cell fraction in xenograft specimens derived from patients with relapsed BPL and significantly improved the EFS outcome of NS mice challenged with drug-resistant human BPL xenograft cells.ConclusionThe CD22-RNAi technology is applicable to all BPL patients both high risk and standard risk. That is because CD22ΔE12 is a characteristic feature of drug-resistant leukemic clones that escape chemotherapy and cause relapse in both high risk and low risk subgroups of patients. The technology therefore has the potential (1) for prevention of relapses by selectively killing the clones that are most likely to escape chemotherapy and cause relapse as well (2) for treatment of relapses in BPL. This research project may also lead to innovative salvage regimens against other forms of CD22ΔE12-positive relapsed B-lineage leukemias and lymphomas.

Therapeutic delivery of siRNA with polymeric carriers to down-regulate STAT5A expression in high-risk B-cell acute lymphoblastic leukemia (B-ALL)

“…RNAi has become a promising therapeutic approach for cancer and its success can be observed at various stages of clinical trials in different malignancies [31][32][33]. In ALL, various targets have been successfully silenced by RNAi, such as Polo-like kinase 1 (Plk1), CD22ΔE12, CSF1R, JAK1 and FLT3 [34][35][36], all of which highlights the versatility of using RNAi for ALL therapy. In this study, we have shown the feasibility of siRNA-mediated STAT5A silencing in ALL cell lines SUP-B15 and RS4;11 as well as ALL patient cells from different donors, using 2 lipid modified PEIs.…”

Tyrosine kinases in KMT2A/MLL-rearranged acute leukemias as potential therapeutic targets to overcome cancer drug resistance

“…A recently published working database, including data on primary leukemia cells from 201 adult patients with B-ALL (GSE13159), 119 pediatric patients with B-ALL (GSE11877 and GSE13351) and 97 infants with B-ALL (GSE68720), as well as 74 normal/non-leukemic control bone marrow samples (GSE13159), was built with previously archived datasets from the NCBI repository and used in our comparative gene expression analyses, as previously described in detail [20] . We also used a working database derived from archived datasets including data on primary leukemia cells from 542 adult patients with AML (GSE13159), and 279 pediatric patients with AML (GSE19577, GSE17855) along with the 74 normal/non-leukemic control bone marrow samples (GSE13159) in our comparative gene expression analyses, as described [20] . Probeset level normalization procedures were used as previously reported [20] .…”

“…We also used a working database derived from archived datasets including data on primary leukemia cells from 542 adult patients with AML (GSE13159), and 279 pediatric patients with AML (GSE19577, GSE17855) along with the 74 normal/non-leukemic control bone marrow samples (GSE13159) in our comparative gene expression analyses, as described [20] . Probeset level normalization procedures were used as previously reported [20] .…”

“…Our analyses for ALL and AML focused on the expression levels (interrogated with 64 probesets) of the following 21 PTK genes: ERBB1, FGR, FLT3, FYN, HCK, JAK2, LCK, LYN, MERTK, SRC, BLK, BMX, BTK, ERBB2, ERBB3, JAK1, JAK3, PTK2, SYK, TEC and TYK2. Standard statistical methods, including mixed model ANOVAs, and hierarchical clustering method were employed, as reported [20][21][22][23][24] .…”

Cancer drug resistance: a new perspective

“…They describe a novel approach to reverse resistance by the use of siRNA, which suppresses the survival pathway in which RAS plays a role. Uckun and Qazi [18] also used siRNA (encapsulated in nanoparticles) to downregulate the CD22∆ E12 in drug-resistant B-precursor ALL. Unfortunately siRNA application is still limited to model systems, although carriers such as nanoparticles are being investigated to solve this problem.…”