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normal C57/BL6 mice, the MM tumor becomes more proliferative and extends to extramedullary sites, and thus is a model for relapsed refractory human MM. Serum protein
electrophoresis is used to detect the amount of
tumor-specific monoclonal immunoglobulin.
Therefore, it is possible to monitor the amount
of tumor during the course of disease even
when the tumor is dispersed at different intraand extramedullary locations.
Using the Vk*MYC GEMM of MM,
Chesi et al have determined the effects of
single drugs with both known and unknown
clinical activity on MM tumors that have
arisen spontaneously. Significantly, they find
that 4 of 6 clinically effective drugs (67%) are
active in this model. Equally important is that
7 of 8 clinically ineffective drugs (88%) are
inactive in this model, even though many of
these ineffective drugs have activity against
HMCLs. In addition, they provide evidence
that the more aggressive disease that models
end-stage drug-resistant MM responds only
to combinations of drugs with single-agent
activity against de novo Vk*MYC MM.
Similar to the other GEMM models described above, the Vk*MYC GEMM model of
MM is extremely promising for identifying
both single agents and combinations of existing and new therapeutic agents that are more
likely to be effective in clinical trials designed
to investigate different disease stages. One
caveat is that the MM tumors in this model
may not fully model some kinds of MM tumors (eg, tumors with t(4;14) translocations).
However, unlike most of the other preclinical
models used for MM (Table 1), these tumors
appear to be localized in the appropriate microenvironments in immunocompetent hosts.
Therefore, the Vk*MYC GEMM model also
appears to be suitable for identifying therapies
that target the interaction of tumor cells with
the microenvironment, and immunomodulatory therapies.
Conflict-of-interest disclosure: The author
declares no competing financial interests. ■
REFERENCES
1. Chesi M, Matthews GM, Garbitt VM, et al. Drug response in a genetically engineered mouse model of multiple
myeloma is predictive of clinical efficacy. Blood. 2012;
120(2):376-385.
2. Sellers WR. A blueprint for advancing genetics-based
cancer therapy. Cell. 2011;147(1):26-31.
3. Rubin EH, Gilliland DG. Drug development and clinical trials-the path to an approved cancer drug. Nat Rev Clin
Oncol. 2012;9(4):215-222.
4. Singh M, Lima A, Molina R, et al. Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models. Nat Biotechnol. 2010;28(6):585-593.
5. Chen Z, Cheng K, Walton Z, et al. A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response. Nature. 2012;483(7391):613-617.
6. Shultz LD, Ishikawa F, Greiner DL. Humanized mice
in translational biomedical research. Nat Rev Immunol.
2007;7(2):118-130.
7. Mitsiades CS, Mitsiades NS, Bronson RT, et al. Fluorescence imaging of multiple myeloma cells in a clinically
relevant SCID/NOD in vivo model: biologic and clinical
implications. Cancer Res. 2003;63(20):6689-6696.
8. Yata K, Yaccoby S. The SCID-rab model: a novel in
vivo system for primary human myeloma demonstrating
growth of CD138-expressing malignant cells. Leukemia.
2004;18(4):1891-1897.
9. Calimeri T, Battista E, Conforti F, et al. A unique
three-dimensional SCID-polymeric scaffold (SCID-synthhu) model for in vivo expansion of human primary multiple
myeloma cells. Leukemia. 2011;25(4):707-711.
10. Menu E, Garcia J, Huang X, et al. A novel therapeutic
combination using PD 0332991 and bortezomib: study in
the 5T33MM myeloma model. Cancer Res. 2008;68(14):
5519-5523.
● ● ● MYELOID NEOPLASIA
Comment on Eisfeld et al, page 249
Small
gene, big number, many effects
---------------------------------------------------------------------------------------------------------------George A. Calin and Marina Konopleva
THE UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER
In this issue of Blood, Eisfeld and colleagues present strong evidence that miR-3151
and the host gene BAALC (brain and acute leukemia, cytoplasmic) concomitantly
affect the outcome of patients with cytogenetically normal acute myeloid leukemia
(CN-AML) by influencing significant signaling pathways.1
ver the past decade, molecular oncology
research has revealed that abnormalities
in both protein-coding genes (PCGs) and noncoding RNAs (ncRNAs) can be identified in
O
240
tumors and that the interplay between PCGs
and ncRNAs is causally involved in the initiation, progression, and metastasis of human
cancers.2 MicroRNAs (miRNAs), which are
among the most studied ncRNAs, are small
19- to 25-nucleotide genes involved in the
regulation of PCGs and other ncRNAs, and
some are involved in AML pathogenesis (see
table). MiRNAs are strongly conserved among
distantly related organisms (including invertebrates, vertebrates, and plants), and a large
fraction are located in introns of PCGs. A
plethora of studies in recent years proved that
miRNAs are involved in various biologic processes, including cell-cycle regulation, differentiation, development, metabolism, neuronal
patterning, and aging.3 Initially, miRNAs
were identified by standard cloning methods
starting with RNA size separation, while recently a large number of miRNAs were discovered using various small-sequencing platforms.4 Such platforms allow the identification
of genes with restricted cell-specific expression as well as with a low copy number per cell.
Because of the sequential numbering system
for miRNAs, the higher the number of an
miRNA, the more recent the identification and
therefore the higher the probability to be discovered by deep sequencing. This is the case
of miR-3151, identified as expressed in melanocytes5 and childhood acute lymphoblastic
leukemia.6
What makes the study by Eisfeld et al special and significant in the context of the more
than 6000 publications on miRNAs in the past
10 years? First, the type of disease analyzed:
AML is a deadly cancer with median survival
of less than 3 years and has had minimal advances in improvement in survival duration in
the last decade. Within AML, CN-AML comprises the largest subgroup, and it has variable
outcomes, whereby gene mutations and gene
expression signatures segregate patients in
different prognostic categories.7 Despite recent treatment recommendations of the international expert panel, which consider the mutational profile of selected markers (CEBPA,
NPM1, and FLT3-ITD),8 identification of
new molecular markers and, more importantly, understanding of their role as potential
targets for therapy may help to refine the existing risk-adapted stratification schemes and
contribute toward development of individualized treatment strategies. The Ohio State University group has a long track record of significant contribution to the study of miRNA
abnormalities in AML,9 and therefore this
group is well-positioned to translate the bench
findings for the advantage of AML patients.
The large number of analyzed patients (ie,
12 JULY 2012 I VOLUME 120, NUMBER 2
blood
From www.bloodjournal.org by guest on June 15, 2017. For personal use only.
Table 1. Examples of targets of miRNAs abnormally expressed in AML
MicroRNA
Let-7a
miR-10a
Target
Target gene function
RAS
Small GTPase involved in the regulation of cell proliferation, survival, and apoptosis
HOXA1
Homeodomain transcription factor with a role in regulating definitive hematopoiesis
miR-17–5p, miR-20a, miR-106a
AML1
DNA binding subunit of the hematopoietic transcription factor CBF, controlling multiple genes involved in myeloid differentiation
miR-130a
MAFB
Transcription factor involved in the activation of GPII8, a key protein for platelet physiology
miR-223, miR-107
NFI-A
Transcription factor known to regulate genes involved in cell proliferation
c-Kit
Transmembrane tyrosine-kinase receptor for stem cell factor, required for normal hematopoiesis
miR-221 and miR-222
179) from a homogenous subtype of AML, the
de novo CN-AML, confers statistical strength
to the identified new clinical correlations.
A second source of the current paper’s significance is that the authors provide one of the
few examples in which a host PCG and the
miRNA encoded in one of its introns cooperate on the same pathways but are also involved
in independent pathways that seemingly converge on the malignant phenotype. The
BAALC gene is associated with lower response
rates and shorter disease-free and overall survival in CN-AML.10 Although BAALC was
recently shown to block myeloid differentiation in AML,11 its functional role in AML
pathogenesis and mechanisms by which high
expression affects clinical outcomes are not
entirely understood. In the study by Eisfeld
and colleagues, AML patients classified as
high miR-3151 expressors and those characterized as BAALC expressors share MN1,
CD200, and MEIS as differentially expressed
genes. Furthermore, the miR-3151–associated
coding signature expression (meaning the
PCG whose expression is directly, by sequence complementarity, or indirectly, by any
other mechanism, regulated by these
miRNAs) contains genes involved in major
signaling pathways, such as cell-cycle control
and ubiquitination. The authors proved that
2 members of the latter pathway, FBXL20
(F-box and leucine-rich repeat protein 20) and
USP40 (ubiquitin-specific protease 40), are
direct targets of miR-3151, which is per se a
new finding.
Third, probably the most significant finding of this paper is that high expression of both
miR-3151 and BAALC characterize the patients with the lowest (50%) rates of complete
remission (CR) and significantly shorter
disease-free and overall survival. In turn, patients with high expression of only 1 of those
markers had intermediate outcomes, while the
blood 1 2 J U L Y 2 0 1 2 I V O L U M E 1 2 0 , N U M B E R 2
low expressors for both genes had the highest
CR rates. Expression of BAALC was mainly
associated with achievement of CR, while that
of miR-3151 affected the outcomes once CR
had been achieved (disease-free survival), suggesting that the 2 genes contribute to the
biology of the disease through different
mechanisms. Measuring both of these markers
at diagnosis may help to identify upfront
which CN-AML patients have an unfavorable
prognosis and may require more aggressive or
investigational therapies.
Finally, this article is important in particular for the scientists who have been involved in
this field for years. One dogma based on empirical observation is that the miRNAs that are
well-expressed and ubiquitously expressed
would be the most significant players in a signaling pathway (as in the case of miR-21). The
fact that a significant role is played by an
miRNA from the 3000 series identified recently by deep-sequencing experiments at a
modest expression level in a quite restricted
panel of cells, including hematopoietic cells,
suggests that the miRNA genomic galaxy has
more hidden stars that will come into play in
the future. This finding has also a practical
consequence—it demonstrates that the initial
screening studies on patients will have improved identification of potentially significant
miRNAs if done by next-generation sequencing methods.
As with all important studies, the contribution by Eisfeld et al raises many questions that
can boost further investigations: What are the
mechanisms that regulate the expression of the
BAALC and miR-3151 genes? Are these common and related to specific transcription factors? Are FBXL20 and USP40 the most important targets for the mechanism of response
to therapy in AML involving miR-3151, or are
there other more significant targets? Did the
miR-3151 signature correlated with miR-3151
expressor target an overlapping spectrum of
downstream PCGs? Did a 4-gene signature
composed of the pair of miRNA/host genes
and the targets FBXL20 and USP40 separate
patients more or less distinctly than did the
2-gene signature? Certainly, these questions
and others will not be left without answers
for long.
Conflict of interest disclosure: The authors
declare no competing financial interests. ■
REFERENCES
1. Eisfeld A-K, Marcucci G, Maharry K, et al. miR-3151
interplays with its host gene BAALC and independently
affects outcome of patients with cytogenetically normal
acute myeloid leukemia. Blood. 2012;120(2):249-258.
2. Spizzo R, Nicoloso MS, Croce CM, Calin GA. SnapShot: MicroRNAs in cancer. Cell. 2009;137(3):586.
3. Kasinski AL, Slack FJ. Epigenetics and genetics. MicroRNAs en route to the clinic: progress in validating and
targeting microRNAs for cancer therapy. Nat Rev Cancer.
2011;11(11):849-864.
4. Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: approaches and considerations. Nat Rev Genet.
2012;13(5):358-369.
5. Stark MS, Tyagi S, Nancarrow DJ, et al. Characterization of the melanoma miRNAome by deep sequencing.
PLoS One. 2010;5(3):e9685.
6. Schotte D, Moqadam FA, Lange-Turenhout EA, et al.
Discovery of new microRNAs by small RNAome deep sequencing in childhood acute lymphoblastic leukemia. Leukemia. 2011;25(9):1389-1399.
7. Schlenk RF, Dohner K, Krauter J, et al. Mutations and
treatment outcome in cytogenetically normal acute myeloid
leukemia. N Engl J Med. 2008;358(18):1909-1918.
8. Döhner H, Estey EH, Amadori S, et al. Diagnosis and
management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of
the European LeukemiaNet. Blood. 2010;115(3):453-474.
9. Marcucci G, Radmacher MD, Maharry K, et al. MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J Med. 2008;358(18):1919-1928.
10. Schwind S, Marcucci G, Maharry K, et al. BAALC
and ERG expression levels are associated with outcome and
distinct gene and microRNA expression profiles in older
patients with de novo cytogenetically normal acute myeloid
leukemia: a Cancer and Leukemia Group B study. Blood.
2010;116(25):5660-5669.
11. Heuser M, Berg T, Kuchenbauer F, et al. Functional
role of BAALC in leukemogenesis. Leukemia. 2012;26(3):
532-536.
241
From www.bloodjournal.org by guest on June 15, 2017. For personal use only.
2012 120: 240-241
doi:10.1182/blood-2012-05-427393
Small gene, big number, many effects
George A. Calin and Marina Konopleva
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