Enrichment Analysis for Common DEGs shared by MM datasets
We then applied 1439 common DEGs shared by at least 15 MM datasets as an input to do enrichment analysis for the biological process network (Figure 2A), the pathway map (Figure 2B), GO process (Figure 2C), and disease phenotypes (Figure 2D) using MetaCore. Figure 2 displays the top 10 highly related pathways and the top 20 diseases. Not surprisingly, we found that more than half of the top 10 pathway maps and several process networks highly associated with MM datasets are tightly involved in cell proliferation, translation, transcription, and cell cycle, which are important biological pathways that are highly expressed in cancer cells due to the rapid and unlimited cell proliferation potential of cancer cells. In addition to these common pathways induced by multiple cancers, we identified several pathway maps, biological process networks and GO processes that are closely related to immune response and inflammatory signaling, especially IL-6 and B cell antigen receptor (BCR) signaling, which is specific for the immune response caused by B cell. As we mentioned before, multiple myeloma is a type of cancer caused by malignant plasma cell, the DNA impairment in B cell make abnormal B cells, which will turn to malignant plasma cells in response to a stimulus, followed by the release of abnormal antibodies, leading to tissue damage. Thus, the BCR signaling is specific for MM, same as the IL-6 signaling in MM (Figure 2B). Moreover, viral process and parasitism symbiosis are identified as top listed GO process, which indicates a potential correlation between viral infection and multiple myeloma. Furthermore, ubiquitin-proteasome system is also emphasized here in the top ranked pathway maps, which further indicates a role of proteolysis via USP and autophagy in MM, in accordance with our previous analysis.
Figure 2D shows the top 20 diseases that have a higher correlation with common DEGs genes shared by MM datasets. First, many of the other types of neoplasms and carcinoma are intensely connected to MM, as different cancer types often share a lot of similar pathological pathways. They occur in different tissues and organs, including uveal tracts, breast, ducts, lobules, endocrine gland, eye, and ovary. Additionally, we also find some diseases specifically related to multiple myeloma, such as the bone marrow diseases and hematological diseases. Multiple myeloma is a cancer of plasma cells t located in bone marrow, and it is the second most common hematologic malignancy. Interestingly, virus diseases, RNA virus infection, and immunological deficiency syndromes are ranked at the top of the diseases, indicating a tight association and potential common pathological pathways shared by viral infections like HIV and MM, which is consistent with our previous results.
Positive and negative correlated genetic perturbations
Top positively (Figure 3A) and negatively (Figure 3B) correlated genetic knockdown with MM datasets is identified. We identified the significant related genetic perturbations with each MM dataset using Knockdown Altas function in BaseSpace. We then took the highly connected gene perturbations with a gene perturbation score higher than 50 into account for summing up data of 30 MM datasets, finally to determine and rank the highly positive or negative correlated gene perturbations, including gene knockout, knockdown, mutation, and overexpression. Positive correlations between DEGs induced by specific single gene knockdown/knockout and MM induced DEGs may imply that the functions of these genes are impaired during the MM process, because knockdown/knockout of these genes will induce similar gene signature change as MM. Our findings are consistent with other literature reports. Negative correlations between a single gene knockdown/knockout DEGs and MM DEGs might suggest that this gene overexpresses in the MM disease condition, and blocking this single gene expression or inhibiting the protein encoded by this gene will reverse the differentially expressed genes in the pathological process of MM and help treat the disease.
The gene signature induced by ROR1 knockdown is highly negatively connected to the MM induced data. A functional study suggested that ROR1 is a part of Wnt signaling pathway and can promote cancer cell survival[1, 4]. Overexpression of the receptor tyrosine kinase-like orphan receptor 1 (ROR1) has been identified in B-cell chronic lymphocytic leukemia (B-CLL) and chronic myelogenous leukemia (CML), but not in normal white blood cells and reactive lymph nodes, suggesting ROR1 might serve as a tumor-specific target for therapy[4, 5, 1].
SLAMF7 is negatively associated with MM by 16 negative associations and no positive association with 30 MM gene sets. This gene encodes Signaling Lymphocyte Activation Molecule Family member 7 (SLAMF7), a therapeutic target for MM treatment. In 2015, the FDA approved Elotuzumab (a humanized monoclonal antibody targeting SLAMF7) as a treatment for patients with multiple myeloma who have received one to three prior medications[6]. Now Elotuzumab is a global approved medication used in combination with other anti-MM drugs to treat relapsed/refractory and advanced MM. It demonstrates its efficacy to improve symptoms when added into other MM therapy without causing any significant additive toxicity [7].
Another top negatively associated knockdown gene is MTDH (Metadherin), which has shown a crucial role in several aspects of tumor progression, including transformation, evasion of apoptosis, invasion, metastasis, and chemoresistance. MTDH overexpression is frequently observed in different cancers, including melanoma, glioma, neuroblastoma, as well as liver, breast, prostate, and esophagus cancers. Overexpression of MTDH activates downstream PI3K/AKT, NF-κB, and Wnt/β-catenin signaling pathways to stimulate cancer cell proliferation, cell survival, invasion and chemoresistance. MTDH also mediates the transformation of oncogene Ha-Ras and c-Myc and the adhesion of tumor cells to remote organs to promote transformation and metastasis. MTDH can be a therapeutic target to block several processes of tumor progression[8].
Sixteen positive correlations were identified between MM datasets and DEGs induced by knock down of SATB1, “a cell-type specific global gene regulator” by folding chromatin into loop domains[9]. Scientists reported that SATB1 can regulate gene expression to promote growth and metastasis of breast tumors[10]. Here, we observe the reverse correlation between SATB1 and MM: the knockdown of SATB1 has a positive association with MM, indicating blocking of SATB1 might regulate genes to promote MM. Daxx, another gene knockdown with positive correlation with MM, is reached by 14 positive associations with only one negative one. Yang X et al. identified Daxx, as a Fas binding protein that can activate both Fas-mediated apoptosis and JNK signaling pathway[11]. As JNK is a key modulator for cell death, Daxx can activate cell death related pathways, and might have the potential to promote cancer cell death in MM. To summarize, most positive and negative related gene knockdown are in accordance with literature and clinical reports, specific for MM (ROR1 and SLAMF7), or for other types of cancers (MTDH and Daxx). Some highly correlated genes are still not reported with multiple myeloma, indicating a new direction for further investigation.
MM positively correlated gene mutations and gene overexpression are overlapped with each other. Among the top ranked positive related overexpression displayed in Figure 3C, RARA, FGFR1, PML, CD247, MYH11 and CBFB are also found in the top list of positively associated gene mutations (Supplementary Figure 3), which are also demonstrated to be involved in carcinogenesis pathologies. Our analysis suggested that FGFR1 mutations might cause FGFR1 overexpression in tumor cells. Fibroblast growth factor receptor 1 (FGFR1) plays an essential role in the regulation of cell proliferation, differentiation and migration via mediating the downstream signaling, such as MAPK, AKT/PI3K, and Ras. Amplification of FGFR1 was reported in breast cancer[12], non-small[13] and squamous cell lung cancer[14], urinary bladder cancer[15], and prostate cancer[16]. Several tyrosine kinase inhibitors targeting FGFR1 are currently applied for cancer treatment. Regorafenib, Sorafenib, Lenvatinib, Nintedanib and Pazopanib are approved by the FDA to treat carcinoma that occurs in different tissues and organs, such as hepatocellular carcinoma, advanced renal cell carcinoma, non-small cell lung carcinoma and differentiated thyroid carcinoma (DTC) that is refractory to radioactive iodine[17]. Negative correlated overexpression also shares some common gene with negative correlated mutations, such as ZBTB16 and MAPT. ZBTB16 plays a role in myeloid maturation and serves as a component of E3 ligase that mediates protein ubiquitination and subsequent proteasomal degradation for abnormal proteins, which might be associated with acute leukemia and multiple myeloma[18]. MAPT encodes the microtubule-associated protein tau (MAPT), and its mutations have been associated with some neurodegenerative disorders such as Alzheimer's disease, Pick's disease, and frontotemporal dementia.
Positive and negative associations between MM- and drug-induced gene profiles
Significant differentially expressed genes with a score over 50 compared to each MM datasets were collected and summarized for a total of 30 datasets. Top ranked drugs, which induced DEGs with more than 15 negative associations compared to MM induced DEGs among 30 datasets, are listed in Table 1, with their therapeutic targets and number of negative and positive associations. Drugs that induced DEGs with negative correlations with MM, might be able to reverse the gene expression change caused by myeloma, and have a beneficial effect to treat MM. Most of the highly negatively related medications are anticancer agents, anti-infective drugs, dietary supplements and immune regulators.
Table 1. Associations between MM-induced and drug-induced gene profiles
Drug
|
Target
|
Negative Association
|
Positive Association
|
Brefeldin A
|
Protein Transport
|
21
|
6
|
Bafilomycin A
|
Vacuolar-Type H+-ATPase
|
19
|
6
|
Ambroxol
|
Neuronal Na+ Channels.
|
18
|
1
|
GDC-0941
|
PI3K Inhibitor
|
18
|
5
|
Lindane
|
Hair Lice
|
18
|
4
|
Imatinib
|
ABL, C-Kit, PDGFR
|
18
|
3
|
Ergocalciferols
|
Vitamin D2
|
18
|
1
|
Dexamethasone
|
GCR
|
18
|
8
|
Decitabine
|
Nucleic Acid Synthesis Inhibitor.
|
17
|
10
|
Clonidine
|
Α2 Receptors
|
17
|
2
|
Clinafloxacin
|
Topoisomerases DNA Gyrase and Topoisomerase IV
|
17
|
7
|
Gentamicin C
|
30s Subunit of The Bacterial Ribosome
|
17
|
0
|
Geldanamycin
|
HSP90
|
17
|
4
|
Letrozole
|
Orally Active Nonsteroidal Aromatase Inhibitor
|
17
|
4
|
Zardaverine
|
PDE3/4 Phosphodiesterase Inhibitor
|
17
|
3
|
Tetracycline
|
30S Subunit of Microbial Ribosomes
|
17
|
10
|
Perhexiline
|
Mitochondrial Carnitine Palmitoyltransferase-1
|
17
|
2
|
Acrolein
|
Cyclophosphamide metabolite
|
16
|
5
|
Amphotericin B
|
Ergosterol
|
16
|
3
|
Bamipine
|
H1 Histamine Receptor
|
16
|
7
|
Caffeine
|
|
16
|
7
|
Meptazinol
|
Opioid Analgesic
|
16
|
2
|
Mephentermine
|
ADRB1
|
16
|
5
|
Lithium Carbonate
|
|
16
|
5
|
Strophanthidin
|
Na+/ K+ ATPase In Muscle Tissue
|
16
|
9
|
Sirolimus
|
mTOR, Fk Binding Protein-12
|
16
|
9
|
Vincristine
|
Tubulin
|
15
|
10
|
Trihexyphenidyl
|
Antimuscarinic
|
15
|
1
|
Tolazoline
|
Non-Selective Α-Adrenergic Receptor Antagonist
|
15
|
2
|
Sotalol
|
Non-Selective Beta-Adrenergic Receptor Blocker
|
15
|
3
|
Solanine
|
Cholinesterase
|
15
|
2
|
Paclitaxel
|
Tubulin
|
15
|
11
|
Monobenzone
|
Increases the Excretion of Melanin
|
15
|
6
|
Lanatoside C
|
Cardiac Glycoside
|
15
|
9
|
Isoproterenol
|
Non-Selective Β Adrenoreceptor Agonist
|
15
|
6
|
Folic Acid
|
Vitamin B9
|
15
|
7
|
Cyclosporine
|
Immunosuppressant with A Specific Action on T-Lymphocytes
|
15
|
10
|
Among the top listed drugs with negative associations (Table 1), several drugs have already been approved and applied to treat MM or investigated in clinical trials. Table 2 lists nine currently used anti-MM drugs that are also identified from the top list of MM gene negatively associated drugs with their development stage, regimen, number of positive and negative associations, as well as reference. For example, both Imatinib and Ergocalciferols have 18 negative correlations with MM gene datasets and only a few positive correlations (3 for Imatinib, 1 for Ergocalciferols). Imatinib, a tyrosine kinase inhibitor, is now in clinical trial Phase II in a single drug form and in combination use for MM treatment. Ergocalciferol, also known as Vitamin D2, is used in combination with other anti-tumor drugs as a nutrient supplement to treat MM [20]. Another dietary supplement, Folic Acid, also known as Vitamin B9, was also identified to have potential for treating MM, which is consistent with the presence of this drug in clinical trial Phase II for combination use in MM. Dexamethasone, a type of corticosteroid medication with 18 negative associations and 8 positive ones, was approved by the FDA to treat patients with MM in combination with other drugs [21]. Additionally, four other antitumor agents, including Sirolimus, an mTOR inhibitor, Decitabine, a nucleic acid synthesis inhibitor, as well as Vincristine and Paclitaxel, tubulin inhibitors, are in different clinical trial phases (Phase I for Sirolimus and Decitabine, Phase III for Vincristine, Phase II for Paclitaxel) to treat MM along or with other medications.
Table 2. Currently anti-MM drugs induce DEGs highly negatively related with MM
Drug
|
Negative association
|
Positive association
|
Development stage
|
Reference
|
Regimen
|
Imatinib
|
18
|
3
|
Phase II
|
PMID: 16321825
|
Single and combination
|
Ergocalciferols
|
18
|
1
|
Phase II
|
NCT00171925
|
Combination
|
Dexamethasone
|
18
|
8
|
Approved
|
Combination
|
Decitabine
|
17
|
10
|
Phase I
|
NCT00002980
|
Single
|
Sirolimus
|
16
|
9
|
Phase I
|
NCT0069343, NCT01303965
|
Combination
|
Vincristine
|
15
|
10
|
Phase III
|
NCT00002556, PMID:15005346, PMID: 8370422
|
Combination
|
Paclitaxel
|
15
|
11
|
Phase II
|
NCT01646762
|
Single and combination
|
Cyclosporine
|
15
|
10
|
Phase III
|
PMID: 11843823
|
Combination
|
Folic Acid
|
15
|
7
|
Phase II
|
NCT00171925
|
Combination
|
For some of these drugs listed in Table 1, they have the potential to be repurposed for the treatment of multiple myeloma because of their possible capacity to reverse the mRNA level change in pathological conditions. We checked the literature first and did some simple in vitro assays using some easily accessible medications to verify the gene signature analysis prediction for drug reposition.
As shown in Figure 5A, although Folic Acid and Bafilomycin A1 themselves did not have much anti-proliferation effect, the combination use of Folic Acid or Bafilomycin A1 with Bortezomib will improve the anti-proliferation activity of Bortezomib (a well-known FDA approved anti-MM drug) in MM.1.S cells (a multiple myeloma cell line) with 16h medication treatment. The other two drugs predicted to be repurposed for MM treatment in our study, Sorafenib and Doxorubicin, also showed some anti-proliferation effects on the MM cell line. These results indicate that our prediction via gene signature analysis might be reliable on the cellular level.
To further investigate the signaling pathway these medications go through to kill the tumor cells, we treated MM1.S cells with these drugs and their combinations for 16h at their effective concentrations. Then we collected the cell lysate samples and ran Western blot to measure the caspase-3 level changes, a well-known apoptosis marker, influenced by different drugs. As shown in Figure 5B we found that co-treatment of Bortezomib with folic acid or Bafilomycin A1 induced more caspases 3 cleavage (19kDa and 17kDa) than treatment with Bortezomib alone. Bafilomycin A1 and Folic acid also induce caspase 3 cleavage compared with DMSO alone, but the effect is much weaker than bortezomib. In addition, in accordance with our MTT result, treatment of Sorafenib also induces the cleavage of caspase 3, induces apoptosis signaling and causes tumor cell death.
To summarize, both the common DEGs from the MM datasets and the drugs that induce negative correlated transcriptional profiles are involved in the pathogenesis of infection, immune, and inflammatory pathways or are indicated to treat viral, bacterial and fungal infection, inflammation and immune diseases. This result suggests a tight relationship between multiple myeloma and these diseases, from molecular mechanism to therapeutic treatment. It was reported that infections with the HIV and hepatitis C virus appeared associated to an enhanced multiple myeloma risk. Infections are a major threat to multiple myeloma patients, with majority of morbidity and mortality due to infections[31, 32]. In a nationwide population-based study in Sweden, multiple myeloma patients showed a 7-fold risk to develop a bacterial infection and a 10-fold risk for viral infection compared to matched controls (P<0.001)[33, 32]. The complications of MM are the major cause for the death of patients. It is necessary to introduce the anti-infective agents in combination with anti-tumor therapy for the treatment of infectious complications in MM patients, which is consistent with our drug reposition prediction[32].
As for the immune response, multiple myeloma is a cancer of plasma cells, which are cells responsible for producing antibodies in response to foreign antigen and debris stimulations. Multiple myeloma disease will lead to the dysfunction of plasma cells, which is followed by immunodeficiency. It has been shown that MM patients have a low immune response to infection, which is one of the causes for a higher risk of infection[34, 35]. “Managing the complications of the disease and its treatment, such as infections, thrombosis and neuropathy, has become more important as MM patients survive longer[32].” Drugs with negative associated gene signature compared to MM in our study also include cardiovascular agents and CNS medications, suggesting possible medication reposition to treat the complications of the disease.