AML not only represents one of the most fatal leukemias but also ranks among the deadliest of all cancers. It presents a myriad of chromosomal alterations and gene mutations, comprising a clinically heterogeneous group of diseases (Green and Konig, 2020). Cytogenetic abnormalities (chromosomal translocations, deletions, etc.) are found in most AML cases, and strongly correlate with prognosis. Therefore, the modern WHO classification of AML categories and subtypes relies substantially on cytogenetics (Carter et al., 2020). However, unlike molecular features (mutations, overexpressed signaling pathways, etc.) cytogenetic abnormalities usually do not offer molecular targets that allow for development of targeted therapeutics. Therefore, even where cytogenetic aberrations are established and correlated with prognosis, it is important to create a detailed molecular characterization of those subtypes most in need of new targeted therapeutics. Many AML patients are treated using untargeted chemotherapy. This is effective against those cytogenetic subgroups recognized as having good prognosis under that treatment regimen, but chemotherapy offers very low survival rates to those cytogenetic subgroups recognized as having poor prognosis under this treatment regimen, with only about 20% survival beyond 2 years (Cancer Genome Atlas Research et al., 2013). Currently, nine agents have been approved, including FLT3, IDH, Bcl-2 inhibitor, and others. Due to the heterogeneity of AML, there is a need to identifying new molecular targets for future targeted therapies (Kantarjian et al., 2021). In recent years, developments in multi-omics data integration have been useful in identifying new subgroups as well as biomarkers for different types of cancers. Nguyen et al. (2020a), used three-omics profiles, DNA copy number aberration, methylation, and mRNA expression, to discover two biologically distinct subgroups in breast cancer. Zheng et al. (2020), used methylation array data and gene expression data to identify prognostic biomarkers in AML. Nguyen et al. (2020b), used mRNA, Methylation, and miRNA from many types of cancer to develop tools and discover disease subtypes. Therefore, we examined multi-omics data to seek intrinsic molecular subgroups that could guide the development of additional effective targeted therapies for patients with poor prognosis under chemotherapy.

We began with an unsupervised clustering analysis using two types of data: somatic copy number alteration (CNA), and gene expression levels from RNA-seq measurements. We then identified differences among the three resulting clusters in their risk stratification, and in overall survival, using datasets with information on mutations and putative copy number alterations from GISTIC (Genomic Identification of Significant Targets in Cancer), with matched clinical data. Next, we performed pathway analyses to find differences among the three molecular subgroups in which molecular pathways they were enriched in. Further analyses focused on molecular characterization of the one cluster with the worst prognosis under the chemotherapy.

From the MFA analysis, the first 6 PCs were chosen for unsupervised clustering gene set analysis due to the equal contribution of CNA and RNA-seq expression (Figure 1)

Through unsupervised clustering, we selected the three subgroups as the best clustering solution because this number of clusters gave the greatest area under the CDF curve (Figure 2A), and the best separation of clusters (Figures 2B,C). We named the three resulting clusters as follows: C1 or “Intermediate Risk Molecular Subgroup”; C2 or “Low Risk Molecular Subgroup”; and C3 or “High Risk Molecular Subgroup”. Descriptive names were based on our survival analysis (Figure 3A).

The three putative subgroups of cases resulting from our unsupervised clustering analysis (Figure 2) differed from each other both in their prognosis (Figure 3A), with p = 2 x 10⁻⁸ (adjusted by gender and age), and in their molecular traits, with ANOVA test FDR <0.001 (Figure 3B).

These three putative AML subgroups also differed in several other aspects of their molecular makeup (Figure 4).

Among the eleven AML genes we examined, the HRMS subgroup had significantly fewer gene mutations than the other patients. In contrast, among these eleven AML genes, the HRMS subgroup had a higher frequency of copy number alterations (CNAs) (Figure 4). When separated by CNA type, this difference was statistically significant for copy-number amplifications (Fisher exact test, p = 0.014), and for copy-number deletions (p = 0.0001).

The patients in our HRMS subgroup had significantly lower overall survival than did other patients (Figure 3A). This was largely consistent with their risk stratification based on cytogenetics (see Supplementary Table 2). The three new molecular subgroups were significantly associated with established cytogenetic risk stratifications from clinical data (Figure 5; Fisher’s exact test, p value <10⁻¹⁴). Among our samples, most patients with a “poor” cytogenetic risk classification fell within our multi-omics HRMS, while all those with a “good” cytogenetic risk classification fell into other subgroups. As expected, based on this association with established poor cytogenetic risk stratification, HRMS patients had poor overall survival. However, HRMS included only a subset of the poor cytogenetic risk group patients in our dataset (23 of 35 total, see Supplementary Table 2), and this subset had even worse survival than did cytogenetic poor risk patients as a whole set.

In a previous analysis, overall survival of patients in the poor cytogenetic risk group at 2 years was reportedly about 20% (Cancer Genome Atlas Research et al., 2013) (we replicated this result with our subset of 166 of the 200 patients used in the earlier study, P (adjusted by gender and age) = 9E-09, see Supplementary Figure 1). In contrast, among the patients in our HRMS subgroup, overall survival at 2 years was much worse, at only about 10% (Figure 3A).

Hereafter, we focused on the high-risk molecular subgroup (HRMS), because patients in this subgroup had significantly worse clinical outcomes than other patients and did not over-express the drug-target genes for existing targeted therapeutics (IDH1, IDH2, CD33, BCL2, and FLT3). We focused on this molecular subgroup for further molecular characterization in search of promising new drug targets.

The multi-omics pathway analysis using RNA and CNA expression data revealed significant differences among the molecular subgroups in the combined activation gene set scores of various molecular pathways from both datasets (see Supplementary Table 3 for the single gene set scores (GSS) of these pathways). The HRMS subgroup showed higher activation than other patients of most molecular pathways related to immune function, cell proliferation, and DNA damage, with CNA expression contributing more than RNA-seq expression to this overall GS (Figure 3B).

Among genes known to be important in AML, mutation frequencies differed in our HRMS subgroup versus other patients (Table 1). Among the nine AML genes in our mutation data set, most (6/9) had lower mutation frequencies in HRMS than in other patients, but these differences were not statistically significant. In contrast to the other AML genes, TP53 was mutated in most patients in the HRMS, but not in any other patients, constituting a highly significant difference (Table 1).

To shed light on whether currently available drugs might be well suited for each of our molecular subgroups, we examined each subgroup for expression levels of the drug-targeted genes: BCL2 (venetoclax), FLT3 (midostaurin and gilteritinib), IDH1, and IDH2 (enasidenib and ivosidenib), and CD33 (gemtuzumab and ozogamicin) (Carter et al., 2020; Kantarjian et al., 2021). For each of these drug targets, one subgroup showed higher expression than the others. However, among the three subgroups, the HRMS subgroup did not show the highest expression of any available drug target (Figure 6). This suggests a need for new potential drug targets for this subgroup especially. The HMRS subgroup that we identify here based on molecular markers, overlaps substantially with the long-established cytogenetic high-risk subtype (Figure 5), but differs in two important ways. Firstly, after treatment with chemotherapy, The HMRS had even lower survival than does the cytogenetic high-risk subtype. Thus, our molecular subgroup offers a more focused classification of cases that are not successfully treated with chemotherapy, and that therefore urgently need new targeted therapeutics. Secondly, unlike cytogenetic features, which do not offer drug targets, this subgroup is characterized by molecular traits that do offer potential as new drug targets. Our findings indicate several candidates for drug targets specific to the extremely high-risk patients of our HRMS subgroup. These candidate targets include mutations of gene TP53, which was mutated in most HRMS patients (Table 1), as well as overexpression of six genes that were highly over-expressed in the HRMS subgroup, including CD34, CD109, CD200, E2F4, MN1, and MLLT3. Other potential targets may be found in the molecular pathways that are highly activated in our HRMS subgroup (Figure 3B).

One of the strongest molecular associations with our HRMS subgroup was mutations in TP53. This is consistent with the fact that TP53 mutations are known to be associated with cytogenetic abnormalities, and with poor outcomes, as is our HRMS subgroup. It has long been established that TP53 mutations are associated with resistance to chemotherapy and short survival in hematologic malignancies (Wattel et al., 1994). The importance of TP53 mutations specifically for our HRMS subgroup is also consistent with the guidelines of the National Comprehensive Cancer Network, which classify AML patients with normal cytogenetics into the poor/adverse risk category if they harbor TP53 mutations (Daver et al., 2020). In AML, mutations in TP53 are associated with poor responses to chemotherapy, and with very poor prognosis (Wang et al., 2020). These authors (Wang et al., 2020) suggested that it was important to test whether other pathways activated by TP53 mutations could be therapeutically targeted. Our results should contribute to reaching that goal.

The overexpression of MN1 is known to confer resistance to chemotherapy, and a worse AML prognosis. Pardee (Pardee, 2012) investigated the mechanisms for this and suggested that therapies directed at increasing TP53 function may be useful for such patients. Another of the genes most over-expressed in our HRMS subgroup was E2F4. This is unsurprising, as it is known that TP53 mutations can drive the expression of E2F4 (Blandino and Di Agostino, 2018). The over-expression of E2F4 in our HRMS subgroup was also consistent with a recent report that E2F4 over-expression was associated with poor prognosis in AML patients, and that in a mouse model, depleting E2F4 inhibited proliferation and suppressed the growth of AML cells (Feng et al., 2020). These authors suggested E2F4 as a potential therapeutic target (Feng et al., 2020), and here we support that suggestion by showing the importance of this gene specifically in the HRMS subgroup of patients expected to fare worst under untargeted chemotherapy.

Other molecular characteristics of our HRMS subgroup include highly activated molecular pathways in the categories of immune function, DNA damage, and cell proliferation, all three of which are consistent with previous reports. A high level of DNA damage has been reported for cells of AML patients categorized as having high-risk cytogenetics and is accompanied by activation of DNA damage pathway (Cavelier et al., 2009). Our results show that inflammatory response and IL6 JAK STAT signaling pathways were highly activated in HRMS. This is consistent with the findings that the inflammatory pathway leads to an activation of the JAK/STAT signaling in AML which fosters leukemia proliferation (Habbel et al., 2020).

Our results suggest that pathways activated by mutations in TP53 might be targeted therapeutically. We found that the pathways highly activated in our HRMS are in the proliferation category, including, E2F targets, G2M checkpoint, and Myc targets V2 (see Supplementary Figure 2). Activation of these proliferation pathways can be promoted by the overexpression of the E2F4 gene.

Limitation: Our sources provided data on a relatively small sample of cases representing the HRMS subgroup, comprising only, 25 out of 166 cases, which may limit the power of our statistical results, but is unlikely to affect the nature of the qualitative results.

A distinct subgroup of AML patients is not successfully treated with chemotherapy, and urgently needs targeted therapeutics. Potential new drug targets for this subgroup include over-expressed genes E2F4, and MN1, as well as mutations in TP53, and over-activation of several molecular pathways.