Molecular pathway activation features of pediatric acute myeloid leukemia (AML) and acute lymphoblast leukemia (ALL) cells

Results

Profiling of gene expression in leukemia samples

The bone marrow biopsy samples were collected and analyzed for seven pediatric acute lymphoblast leukemia (ALL) and seven pediatric acute myeloid leukemia (AML) samples. The mean ages were 7 and 6 years old (y.o.) for the patients in the AML and ALL groups, respectively. Leukemia samples were compared with the normal peripheral blood isolated from the three healthy donors, with the mean age in the group 12 y.o. (Supplementary dataset S1).

The total RNA preps were extracted, and gene expression was profiled with microarray hybridization. We used original customized microchip system developed using the CustomArray (USA) technology of direct electrochemical oligonucleotide synthesis on the array [15]. Using the CustomArray platform, we synthesized the arrays with 2228 oligonucleotide probes corresponding to 2016 human genes involved in 334 intracellular signaling pathways (Supplementary dataset S2). For the custom microchip, we used original oligonucleotide probe sequences of the Illumina HT 12 v4 platform. The library preparation and hybridization protocol is outlined on Supplementary dataset S3. The microarray hybridization signals were quantile normalized according to Bolstad et al. [16], and gene expression data were deposited in GEO database with the accession numbers GSE84574 and GSE84575.

For the adult leukemia samples, we took GEO dataset GSE37307containing 30 AML and 17 peripheral blood samples profiled using the Affymetrix hgu133a microarray hybridization platform. The gene expression data were quantile normalized and further compared with pediatric samples.

Bioinformatic analysis of gene expression data

We processed the transcriptomic data under investigation to establish normalized cancer-to-normal (CNR) expression rates and to build pathway activation strength (PAS) profiles corresponding to intracellular signaling pathways for each sample. The analysis included 334 pathways (Supplementary dataset S2) and 2016 individual gene products. For PAS measurements, we applied the OncoFinder method which was previously shown to increase the stability of gene expression data [14]. In multiple previous studies, OncoFinder was utilized to analyze human and non-human samples from a broad range of conditions including leukemia, solid cancers, fibrosis, age-related macular degeneration disease, asthma, Hutchinson-Gilford disease and cell culture [17–20]. The formula for PAS calculation for a given pathway (p) is as follows [21]:$$

${PAS}_p=\mathrm{ }\frac{1}{N}\sum _n{ARR}_{np}\bullet {\mathrm{l}\mathrm{g}(CNR}_n)$

The functional role of a gene product in a pathway is reflected here by a discrete flag activator/repressor role (ARR), which equals 1 for an activator, –1 for a repressor, and shows intermediate values -0,5; 0,5 and 0 for the gene products having intermediate repressor, activator, or unknown roles, respectively. The CNR_n value is the ratio of the expression level of a gene n in the sample under investigation to the average expression level in the control sampling. N is the number of individual gene products in the pathway p. The positive value of PAS indicates activation of a pathway, and the negative value - its repression in a biosample under investigation. The CNR and PAS values obtained for the normal and leukemia samples are shown on Supplementary dataset S2.

Comparison of pediatric and adult normal and leukemia gene expression profiles

The CNR and PAS data were analyzed in two ways to identify gene expression and PAS signatures that distinguish (i) pediatric ALL and AML from normal samples and (ii) pediatric AML from the adult AML samples.

Pediatric ALL-specific features

We compared seven pediatric ALL samples with the three normal peripheral blood samples. To identify the characteristic ALL-specific features, we calculated the “area-under-curve” (AUC) values [22] for the CNR and PAS scores of each of the gene products and pathways under investigation. The AUC value is the universal characteristics of biomarker robustness and it is dependent on the sensitivity and specificity of a biomarker. It correlates positively with the biomarker quality and may vary in an interval from 0.5 till 1. The AUC threshold for discriminating good and bad biomarkers is typically 0.7 or 0.75. The entries having greater AUC score are considered good-quality biomarkers and vice-versa [23]. We could identify 94 gene products and 47 molecular pathways that had close to 1 AUC scores for the ALL-normal comparison (Supplementary dataset S4, Table 1). Among those, branches of Akt signaling [24], cAMP [25], cytoplasmic and mitochondrial apoptosis [26], PTEN [27], ATM checkpoint [28], Hedgehog [29], HGF [30], GSK3 [31], Estrogen and Glucocorticoid reception [32,33], IGF1R [34], IL2 [35], TNF [36], ILK [37], JAK-STAT [38], JNK [39], mTOR [40], TGF-beta [41], Ras [42], PPAR [43], NGF [44], VEGF [45], Wnt [46], HIF1 and Notch signaling [47] were previously reported in the literature as ALL-associated pathways. However, the identified GPCR and TRAF-associated apoptosis marker pathways were new, thus representing ~4% of the total ALL-specific pathways.

Table 1. Statistics of the gene expression and pathway activation markers identified in this study.

Comparison	Gene expression markers (GEM)	AUC (GEM)	Pathway activation markers (PAM)	AUC (PAM)
Pediatric ALL vs Normal	94	~1	47	0.90-1
Pediatric AML vs Normal	148	~1	31	0.95-1
Pediatric AML vs Pediatric ALL	139	0.91-1	34	0.92-1
Pediatric AML vs Pediatric ALL vs Normal	172	0.85-0.98	36	0.84-0.96
Adult AML vs Normal	132	0.75-0.95	33	0.75-0.86

Pediatric AML-specific features

When comparing the seven pediatric AML and three normal peripheral blood transcriptomes, we identified 148 marker gene products and 31 molecular pathways with close to 1 AUC scores (Supplementary dataset S5, Table 1). Among them, one top marker pathway (~3%) has not been previously linked with AML: a branch of Inositol-3-phosphate signaling pathway responsible for gene expression with the transcriptional factors CREB3, NFATC2 and MEF2D was found to be strongly upregulated in the AML samples in this study. Of note, AML cells also demonstrated several upregulated p53-related apoptosis-promoting pathways: the branch of Mitochondrial apoptosis pathway related to the activation of p53-dependent gene expression, the branch of P53 signaling pathway responsible for promotion of apoptosis, and the branch of TNF signaling pathway responsible for apoptosis. However, this enhanced upstream regulation of apoptosis was blocked by the strongly suppressed downstream branch of Mitochondrial apoptosis pathway responsible for the irreversible mechanisms such as the DNA fragmentation (Supplementary dataset S5). A similar figure was seen for the ALL cells, were the activation of the upstream cytoplasmic pathway was compensated by the inhibition of the downstream mitochondrial apoptosis pathway (Supplementary dataset S4). This phenomenon most likely refers to the overall ability of leukemic cells to block programmed cell death at the downstream stages.

AML, ALL and normal peripheral blood-specific features

We next identified CNR and PAS biomarker features that can discriminate between the three classes of pediatric biosamples under investigation: AML, ALL and normal peripheral blood cells, with high AUC scores (Supplementary dataset S6, Table 1). We found 172 such gene products and 36 molecular pathways. Among them, GPCR, CREB pathways and a branch of ATM pathway implicated in cell survival mechanisms, were suppressed in the ALL but upregulated in the AML cells.

Comparison of pediatric AML versus ALL samples

To identify the differential molecular features in the AML and ALL samples, we compared seven ALL versus seven AML gene expression profiles. At the level of gene expression, we found 139 biomarkers with high AUC scores (Supplementary dataset S7, Table 1). Remarkably, this list was enriched by the genes related to proteasome target protein degradation (19/139 genes; Fig.1). All of them were expressed at the high levels in the ALL, and at the significantly lower levels in the AML cells, as this was the case for all eleven ubiquitin-specific peptidase (USP) genes from the list, four ubiquitin protein ligase genes (UBR5, UBE2T, UBE2Q1, UBE2L3), and four genes for proteasome subunit A (PSMA). There were also five genes for tubulins and associated proteins that were all significantly upregulated in the ALL compared to AML. The same trend was observed for the four tumor necrosis factor (TNF) superfamily genes and their receptors (TNFSF9, TNFSF13B, TNFRSF10, TNFRSF11).

Figure 1. Ubiquitin-dependent proteasome protein degradation pathway shown as an interacting network. Pathway activation features are shown for the averaged pediatric ALL, pediatric AML and adult AML transcriptomes. Up-regulated nodes are shown in green, down-regulated - in purple, color legend is provided at the bottom. Saturation of the color is proportional to logarithm of cancer-to-normal (CNR) expression rate for each node of the pathway.

In contrast, all five genes from the list for G protein subunits (GNA, GNB and GNG family genes), five genes for fibroblast growth factor (FGF) proteins and three glutamate metabotropic receptor (GRM) genes were upregulated in the AML versus ALL samples.

At the molecular pathway level, we identified 34 top features (Supplementary dataset S7, Table 1), including TNF signaling pathway, Ubiquitin-proteasome pathway (Fig.1), branches of ATM, cAMP, Estrogen, GPCR, HIF-1 alpha, ILK, IP3, MAPK, WNT, and other signaling pathways.

For the first time, our data clearly suggest, that the ALL cells are highly enriched in the proteasomal activities compared to the AML cells. In turn, the AML cells are enriched in GPCR (G protein-coupled receptor) signaling. Those molecular differences clearly seen at both levels of (i) individual gene products and (ii) molecular pathways, may help to decode the mechanisms for greater curability of the ALL tumors and provide avenues for finding new molecular targets for treating AML in the future. In addition, these data suggest that using of proteasomal inhibitors like Bortezomib may be beneficial also for the treatment of the pediatric patients with the ALL, not only for the AML patients, as this is the case now in several clinical studies [e.g., [48]].

Comparison of the adult and pediatric AML-specific features

We next compared 30 and 17 transcriptomes obtained for the adult AML and for the adult normal peripheral blood samples. At the gene expression level, we identified 132 top ranking biomarkers, and 33 – at the pathway level (Supplementary dataset S8, Table 1). These molecular pathways were known to be AML-related and included branches of the AHR, ATM, cAMP, FLT3, HGF, ILK, JAK-STAT, Ras, WNT and other signaling pathways.

In contrast to the pediatric AML samples, where several pathways promoting apoptosis were activated, but blocked at the downstream stages (Supplementary dataset S5), in the adult AML cells the only top pathway related to apoptosis (mitochondrial apoptosis pathway) was repressed instead (Supplementary dataset S8). For the top marker genes, we found seven coincidences for both AML types (Table 2), which were all, to our knowledge, not known to be associated with the AML before: CAMK2B for Calcium/calmodulin-dependent protein kinase type II beta chain, EIF4B for translation initiation factor 4B, HAPLN1 for hyaluronan and proteoglycan link protein 1, HIST1H3B for histone cluster 1, H3b, LIPE for hormone sensitive Lipase E, MAPK13 for mitogen-activated protein kinase 13, and SAR1B for secretion-associated Ras-related GTPase 1B.

Table 2. Statistics of the commonly and oppositely regulated gene expression and pathway activation markers in the pediatric and in the adult AML.

Matches	Gene expression markers	Pathway activation markers
Complete matches - concordant	(7) CAMK2B, EIF4B, HAPLN1, HIST1H3B, LIPE, MAPK13, SAR1B	(2) CD40 pathway (cell survival), Ras pathway (CDC42 signaling)
Complete matches - discordant	(4) BID, F2, JAK3, PPARA	(1) ATM Pathway (Cell Cycle Checkpoint Control)
Incomplete matches – concordant	Not applicable	(14) EGF Pathway (Cell Survival), EGF Pathway (IP3 Signaling), EGF Pathway (Rab5 Regulation Pathway), Glucocorticoid Receptor Pathway (Cell Cycle Progression), Glucocorticoid Receptor Signaling Pathway (Gene Expression via CREB3, STAT5B, SLC22A2, POU2F1), JAK-STAT Pathway, JAK-STAT Pathway (Nml, SOCS, BCL-XL p21, Myc, Nos2, Gene Expression via STAT2), JAK-STAT Pathway (Gene Expression via MYC), Mitochondrial Apoptosis Pathway (DNA Fragmentation), Mitochondrial Apoptosis Pathway (Apoptosis), Ras Pathway (Receptor Endocytosis), Ras Pathway (Increased T-cell Adhesion), TGF-Beta Pathway, TGF-Beta Pathway (Transciption, Arrested Growth, Apoptosis)
Incomplete matches - discordant	Not applicable	(17) ATM Pathway, ATM Pathway (S-phase progression), ATM Pathway (Apoptosis), ATM Pathway (Apoptosis and Senescence), ATM Pathway (Checkpoint Activation), ATM Pathway (G2-Mitosis progression), ILK Pathway (Cell Adhesion), ILK Pathway (Opsonization), ILK Pathway (Cell Cycle, Proliferation), ILK Pathway (Cell Migration, Retraction), Mitochondrial Apoptosis Pathway (Gene Expression via TP53), NGF Pathway (Actin Polymerization, Neurite Outgrowth and Differentiation), NGF Pathway (Neurite Outgrowth and Differentiation), PPAR Pathway, PPAR Pathway (Adipocyte Differentiation, Glucose Homeostasis and Macrophage Function), VEGF Pathway, VEGF Pathway (Actin Reorganization).

Overall, the top expression biomarkers for the pediatric and adult AML were not highly overlapping, thus producing only two completely matching commonly regulated top pathways: a branch of the CD40 pathway influencing cell survival, and a branch of the Ras pathway affecting CDC42 pathway, which were commonly downregulated and upregulated, respectively, in both types of AML. Both pathways have been previously reported to be associated with the AML [49,50]. In addition, different branches of the EGF, Glucocorticoid receptor, JAK-STAT, Mitochondrial apoptosis, Ras and TGF beta pathways were also regulated congruently.

However, the branches of the ATM pathway, ILK signaling, NGF, PPAR and VEGF pathways were regulated oppositely in the adult and pediatric leukemia samples (Table 2). These data evidence that the pediatric and adult AML cells differ greatly in gene expression and in molecular mechanisms used to suppress apoptosis and cell cycle arrest, and to promote growth and proliferation.

Conflicts of Interest

The authors have no conflict of interests to declare.

Funding

This work was supported by the Russian Science Foundation grants no. 14-14-01089 and 14-50-00060 (for M. Suntsova, P.Spirin, V.Prassolov and Anton Buzdin), by the Pathway Pharmaceuticals (Hong-Kong) and First Oncology Research and Advisory Center (Russia) Joint Research Initiative and by the Program of the Presidium of the Russian Academy of Sciences “Dynamics and Conservation of Genomes” (for other authors).

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