BSc in microRNAa as diagnostic biomarkers for cutaneous melanoma

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Riya Verma
1
INTRODUCTION
Melanoma is an increasingly important health problem, and has seen a dramatic increase in
incidence over the past decades. The incidence has tripled during the last 20 years in the
white population. The aggressive nature of the tumour constitutes poor prognosis if
untreated. However if detected and excised in early stages the 5-year survival rate is high.
Table 1 shows 5-year survival rate with increasing American Joint Cancer Committee (AJCC)
staging.
Melanoma AJCC Stage 5-year survival rate (%)
0
100
I
99
II
70
III
25
IV
9
Table 1: 5-year survival rates for melanoma AJCC stage.
Interestingly, whilst melanoma incidence is on the increase, melanoma mortality rates have
been relatively constant. This suggests there is an over-reporting of melanoma. Although
detection of melanoma has improved, where both doctor and patient have become more
vigilant, more accurate diagnosis of melanoma is required.
1.1.1 CLINICAL DIAGNOSIS
The ABCD criterion is used in clinical practice when examining melanocytic lesions. It
assesses four parameters; symmetry, border, colour and diameter.
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Figure 1: ABCD criteria, for comparing benign and malignant lesions. Melanoma lesions are
assymetrical and have an irregular border. They display either 2 or more shades of colour, or change
colour over time. They also have a larger diameter than naevus lesions, >6mm thickness.
1.1.2 AMBIGUOUS LESIONS
Histopathologists are very proficient at diagnosing most suspicious melanocytic lesions.
However a minority of these cases are harder and sometimes impossible to diagnose,
making it difficult for even expert dermatopathologists to classify them. These lesions are
known as ambiguous melanocytic lesions. They display histological features which are
specific to both naevi and primary melanomas, meaning they cannot be distinguished.
Ambiguous lesions present diagnostic uncertainty for patients and clinicians, and are thus
referred to as ‘melanocytic lesions of uncertain malignant potential’.
Within diagnostic histopathology, pathological diagnosis of melanoma is one of the most
difficult. Misdiagnosis of melanoma is the most common cause of medico-legal action
against pathologists. The safest option might be to label it as melanoma, but this can lead to
over-diagnosis.
Misdiagnosing ambiguous lesions would consequent in patients receiving inappropriate
management; either potentially invasive or insufficient treatment. They will also experience
the emotional stress associated with a diagnosis of cancer. Unfortunately, pathology it is not
an exact science Molecular testing has the potential to assist in diagnosing ambiguous
lesions and could thus minimise the negative implications of misdiagnosis.
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1.1.3 MICRO-RNAS
MicroRNAs (miRNAs/miRs) are single stranded, non-coding, RNA molecules approximately
20-23 nucleotides in length. They regulate gene expression by inhibiting messenger RNA
(mRNA) expression post transcriptionally, at the level of translation. MiRs can silence gene
expression via two ways; by repressing translation or by cleaving the mRNA.
The process of microRNA biosynthesis is illustrated and described in Figure 2.
Figure 1: Illustrating the step-wise process of microRNA biosynthesis.
1: the miRNA gene is transcribed by RNA polymerase II to produce Pri-miRNA. 2: Drosha
converts pri-miRNA to pre-miRNA. 3: The pre-miRNA is transported out of the nucleus into
the cytoplasm via a protein called Exportin 5. 4: The pre-miRNA is cleaved by Dicer to a
double stranded RNA molecule, also known as the miRNA-miRNA* duplex. 5: One of the
strands, the miRNA* strand, is degraded 6: The miRNA single strand is loaded onto the RISC
complex, mediated by Dicer. 7: Gene expression is silenced via one of two ways; 7a: If there
is perfect complementarity between the miR and mRNA, then translation is repressed. 7b: If
there is imperfect binding between the miR and mRNA, the mRNA is cleaved and degraded.
MicroRNAs regulate many biological processes, such as growth, apoptosis, and
differentiation. Expression profiling of miRs revealed different expression profiles in cancer,
and studies have shown microRNA misexpression to correlate with many human cancers, as
well as prognosis and survival. This suggests that miR expression contributes to disease
pathophysiology, and they can function as tumour suppressors or oncogenes in cancer. Real-
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time PCR is the gold standard for gene expression quantification, and for microRNA
expression profiling it is one of the most commonly used methods.
The first indication that microRNAs could be involved in human cancer came from Calin et al,
who found two miRs to be down-regulated in chronic lymphocytic leukaemia. Since then
microRNA expression profiles have been associated with many cancers, including melanoma.
For example, MiR-221 and 222 are important in melanoma progression. The most recent
study Sand et al compared miR expression between malignant melanoma, primary
melanomas, and benign naevi, and identified many differentially expressed microRNAs.
1.1.4 DICER AND MITF
Dicer is an RNase III enzyme involved in microRNA biogenesis. It is shown to regulate
biological processes such as differentiation in mice embryonic stem cells, and is essential for
melanocyte survival.
Dysregulation of microRNA processing due to Dicer levels has been associated with
tumourigenesis. Studies have shown both up- and down-regulation of Dicer in many cancers,
such as the lung, liver and prostate. Within melanoma, Jafarnejad et al discovered reduced
cytoplasmic Dicer levels in metastatic melanoma and suggested an inhibitory role for Dicer in
melanoma progression. It associated high levels of Dicer with improved patient survival and
lower AJCC melanoma staging.
Micropthalmia-associated transcription factor (MITF) is a transcription factor that controls
melanocyte development. Processes such as melanocyte proliferation, differentiation and
pigmentation are also under MITF regulation. Upon UV light exposure, increased activity of
MITF stimulates melanocyte differentiation and melanogenesis, allowing the melanin to
protect the cell against nuclear damage.
Within melanoma, a rheostat model is proposed which suggests that levels of MITF change
during melanoma development. It describes how different levels of MITF promote
phenotype switching to distinct melanoma cell phenotypes, as shown in figure 3. High MITF
levels results in cellular differentiation; these differentiated cells are non-proliferative and
non-invasive. Medium MITF levels results in a proliferative phenotype, allowing the cell to
enter the cell cycle again, and further low MITF levels are found in invasive phenotypic cells.
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Figure 1: The Rheostat model, describing the effect of different MITF expression levels on
melanoma cell phenotype.
The model suggests a progressive decrease in MITF levels, from normal or benign
differentiated cells, to melanoma and metastatic disease. There are many studies which
support the rheostat model. Reduced MITF expression has been shown to promote
tumorigenesis, whilst Salti et al discovered that high MITf levels reduced melanoma
invasiveness and improved prognosis. High MITF levels have also been found to promote cell
cycle arrest and prevent cell proliferation by up-regulatingcyclin-dependent kinase inhibitors
P16 and P21.
An interesting link between Dicer and MITF was reported by Levy in 2010. He found that
when MITF levels rise during melanocyte differentiation, Dicer expression is induced,
suggesting that upregulation of Dicer is MITF dependent.
1.2
1.
2.
1.3
HYPOTHESIS
Candidate microRNAs can be used as potential diagnostic biomarkers to distinguish
common acquired naevi from melanoma.
Dicer, the enzyme involved in microRNA biosynthesis, and MITF, a transcription factor
regulating melanocyte differentiation, can also be used as potential diagnostic
biomarkers to distinguish common acquired naevi from melanoma.
AIMS
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1. To identify potential candidate microRNAs which are differentially expressed between
naevi and melanomas.
2. To compare the levels of microRNAs between naevi and primary melanomas, and to
validate their expression levels.
3. To compare the levels of MITF and Dicer between naevi and primary melanomas.
4. To evaluate the diagnostic ability of the biomarkers.
5. To investigate any associations between microRNAs and MITF and Dicer.
1.4
OBJECTIVES
1.
2.
3.
4.
5.
Analyse datasets and array data to identify potential candidate microRNAs.
Measure microRNA expression in the case samples using quantitative real-time PCR.
Validate microRNA expression on a new cohort, also using real-time PCR.
Quantify levels of MITF and Dicer using immunohistochemistry.
Evaluate the sensitivity and specificity of the biomarkers using Receiver Operator Curve
analysis.
6. Assess any correlations/associations between the levels of microRNA with MITF and
Dicer levels.
2
METHODS
2.1 IDENTIFYING CANDIDATE MICRO-RNAS
2.1.1 DATASET ANALYSIS
The Department of Cancer Studies and Molecular Medicine has collated different miRNA
array data over the years. For this project, a dataset comparing miRNA expression in naevi
and melanomas was analysed. Taqman low density array experiments (ABI card A) were run
by Dr. Shona Elshaw to compare expression of 384 miRs between naevi and melanoma
samples.
GEO Datasets on the National Center for Biotechnology Information (NCBI) were searched to
identify the datasets relevant to melanoma and miRNAs(83). The “Analyse with GEO2R” tool
allows identification of differentially expressed genes between two or more groups and was
used in this study to compare miR expression between melanomas and naevi. The
significantly up/down-regulated miRs were identified (adjusted p value <0.05).
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2.2 MELANOMA CASES
The discovery set for this study consisted of 17 naevi and 19 melanoma cases. They were
FFPE tissue selected by histopathologist Dr. Gerald Saldanha from University Hospital
Leicester NHS Trust’s Histopathology Archive. All cases had been diagnosed as naevi or
melanomas by consultant pathologists, and were selected for this study based on the
eligibility criteria shown in Table 3-1.
Inclusion criteria
Exclusion criteria

Sufficient amount of tissue available in the
block from which the sections were cut

A sectional area less than 25 mm2

Sufficient RNA for use in PCR reactions

Ambiguous diagnosis
Table 2-1: Eligibility criteria for selecting the cases in the discovery set.
The discovery set consisted of melanomas with varying breslow thicknesses, from 0.4-19
mm. They were classified into two groups of thin and thick melanomas, using a cut off for
breslow thickness at 2mm (thin melanomas <2mm, thick melanomas >2mm). The thin and
thick melanoma cases were compared separately with naevi, as they are believed to behave
differently biologically. Thin melanomas are rarely metastasise whilst thick melanomas at a
high risk of metastasising.
A second cohort, the validation set, consisted of 15 naevi, 16 metastases and 37 primary
melanomas. The samples were gifted in the form of complementary DNA (cDNA) by Dr
Shona Elshaw for use in this study. Of the 37 primary melanomas in this cohort, 20 were
without metastases (P-M) and 17 with metastases (P+M). They all however had had a
breslow depths of >2mm, and were classified as thick.
2.3 REAL-TIME POLYMERASE CHAIN REACTION
The FFPE tissue was manually microdissected using Tris/SDS buffer, and digested with
Proteinase K (Roche, 10mg/ml) at 55⁰C overnight. The digested tissue was submerged in 500
µl of Trizol and RNA was extracted using chloroform and a series of microfuging washes.
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Figure 2-1: Illustrating the RNA extraction protocol.
The cDNA was reverse transcribed. TaqMan megaplex priming and pre-amplification was carried out
during reverse transcription. Megaplex produces cDNA, on which any miRNA assay (from pool A) can
be run in a PCR reaction. This avoided having to produce separate cDNA for each specific miRNA. Preamplification larger quantity of cDNA to be produced which can be used for many reactions.
The cDNA was mixed with TaqMan mastermix, and amplified during real-time PCR. The thermal
profile entailed 40 cycles of: 50⁰C for 2 minutes, 95⁰C for 20, 95⁰C for 1 second, and finally 60⁰C for
20 seconds.
The Cycle Threshold (CT) is the number of amplification cycles required to reach a threshold level of
fluorescence intensity. The CT values are dependent upon the amount of target nucleic acid
sequence present in the test sample, which becomes amplified in each cycle. High expression of a
microRNA means a large amount of starting miR sequence is present, and so fewer cycles of
amplification would be required to reach the threshold fluorescence. Hence a lower CT value will
result. MiR-191 was the endogenous control in this study, against which expression of
candidate miRs was normalized. It adjusts for experimental variations between the PCR runs.
Data is normalised by calculation of the Delta CT (ΔCT).
Eq.1:
ΔCт = CтmiR-candidate – CтmiR-191
Fold changes, which are a measure of relative quantification (RQ), were calculated for each
miR via the delta delta CT method:
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Eq. 2:
ΔΔCт = ΔCтmelanoma – Mean ΔCт naevi
The ΔΔCт was then used to calculate RQ, using Equation3:
Eq.3:
RQ = 2-ΔΔCт
2.4 IMMUNOHISTOCHEMISTRY
Antigen retrieval was performed on FFPE tissue after dewaxing in xylene and rehydration
with decreasing grades of I.M.S concentrations. This process re-exposes the antigenic
epitopes, which became masked during tissue fixation. The sections d in Tris EDTA (TE)
buffer (10mM Tris and 0.1mM EDTA, pH 9), and microwaved at full power for 20 minutes.
They were left to cool for 30 minutes before immersing them in cold water.
In this study, an indirect immunohistochemical technique was employed to stain tissues,
using the Novolink Polymer Kit (250 tests). The staining process requires the reagents of the
Novolink kit. Firstly, the sections were treated with peroxidase block to neutralise
endogenous peroxidase activity, followed by the Protein block. The primary antibody,
diluted in blocking solution (50ml TBS, 50µl Triton X, 1.5g bovine serum albumin), was
applied to the tissue section and left overnight at 4⁰C. The primary antibodies used in this
study were Abcam anti-Dicer 13D6 ChIP Grade (ab14601) and Abcam anti-MITF C5
(ab12039).
On the following day, the slides were treated with Post Primary block for 30 minutes,
followed by the Novolink polymer which acts as the secondary antibody and binds
specifically to the tissue-bound primary antibody. In between each step mentioned above
the slides were washed in TBS, to remove unbound antibody and unwanted solutions.
The DAB chromogen was added, to react with the peroxidase enzyme on the Novolink
polymer to produce a solid brown precipitate. All sections were stained using Haematoxylin
and dehydrated through increasing I.M.S concentrations. To finish, they were mounted with
DPX resin mountant onto coverslips.
2.5 STATISTICAL ANALYSIS
The data generated in this study was graphically illustrated on the GraphPad Prism Version
5.00 software. The one-way ANOVA statistical test was performed to test for statistical
significance. One-way ANOVA is a parametric test used to compare 3 or more groups. A p
value of <0.05 was defined as significant.
Receiver Operator Characteristics (ROC) curves were generated on Prism and area under the
curve (AUC) values calculated. These values were used to evaluate sensitivity and specificity.
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3
RESULTS
3.1 CANDIDATE MICRORNAS
Analysis of datasets from GEO database (NCBI) and from the Department of Cancer Studies
and Molecular Medicine allowed identification of potential candidate miRs.
The potential down-regulated miRs included miR-204 and 211. The potential up-regulated
miRs included miR-22, 21 and 135b. Table..illustrates the GEO datasets in which the upregulated miRs were identified.
GEO Datasets
MiR
GSE25653
21
22
135b
GSE18509
GSE19387



GSE35579
Freq
GSE24996



GSE35389
N vs M




4
3
3
Table 2: Showing the frequency of the top up-regulated miRs identifiedfrom all 6 GEO
datasets. The ‘freq’ describes the number of GEO datasets in which the miRs were identified
as significantly up-regulated.
3.2 CELL LINE EXPERIMENTS
The cell line experiments with miR-204 and 211 suggested that expression of these microRNAs was
specific to the melanocyte lineage. They both displayed much higher expression in the melanoma cell
lines compared to the non-melanoma cell lines, and both displayed the highest expression in the
melanocyte (see Figure 3).
Log scale
MiR-204 and 211 expression in cancer cell lines
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Figure 3: Relative Quantification of miR-204 and 211 in non-melanoma and melanoma cell
lines
3.3 DOWN-REGULATED MICRORNAS
Real-time PCR found down-regulation of miR-204 and 211 in both thick and thin melanomas in the
discovery set (p<0.0001).
The validation set confirmed the down-regulation of miR-204 and 211 (p<0.0001).
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3.4 UP-REGULATED MICRORNAS
MiR-22 was found to be significantly up-regulated in the thick and thin melanomas
(p<0.0001) in the discovery set. MiR-135b and 21 were significantly up-regulated in the thick
melanomas compared to naevi (p=0.0011 and 0.0016 respectively) but no significant
expression was observed with the thin melanomas.
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The findings from the validation set confirmed the up-regulation of miR-22, 21 and 135b
(p<0.0001).
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3.5 DICER AND MITF
Following immunohistochemistry, the sections were photomicrographed (Leica Biosystems)
and analysed on Aperio Image-Scope software to calculate IHC scores.
The software’s Positive Pixel Count Algorithm (9.1) was used to define intensity ranges for
strong, medium and weak staining. The Algorithm counts the pixels in each intensity range.
IHC score was calculated as strong + medium staining / total positive staining.
****
****
****
****
Figure 4: MITF and Dicer staining between naevi, thin and thick melanomas.
The reduced levels of Dicer and MITF in thin and thick melanomas, compared to naevi, were
found to be statistically significant (p<0.0001).
Since it has been reported that MITF directly regulates Dicer expression, it is likely that
correlations exist between Dicer and MITF levels. Correlation coefficient (r) was calculated
for the scatter graph on Prism which plotter Dicer and MITF IHC scores
The r value was found to be 0.737 (p<0.0001), indicating a significant positive correlation
between Dicer and MITF levels.
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3.6 EVALUATING BIOMARKERS
The AUC values for ROC curves generated in Prism are shown in Table 3.
MiR
Discovery set
AUC
P value
Validation set
AUC
P value
204
211
22
21
135b
1
0.969
0.956
0.827
0.628
<0.0001
<0.0001
<0.0001
0.00077
0.00065
0.971
1
1
0.968
1
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
MITF
Dicer
0.9379
0.9673
<0.0001
<0.0001
N/A
N/A
N/A
N/A
Table 3: AUC values from ROC analysis on the biomarkers. The best AUCs in this study were
produced from miR-204, 211 and 22. Each has displayed either perfect or near perfect AUCs.
4
DISCUSSION
One purpose of this study was to identify candidate miRs and to measure their expression in
the discovery and validation set. Potential microRNAs were identified by analysing GEO
datasets. Real-time PCR carried out on the discovery set found significant down-regulation of
miR-211 and 204, and up-regulation of miR-22, in thin and thick melanomas. MiR-21 and
135b were also significantly up-regulated in the thicker, but not thinner, melanomas.
Findings from the validation set confirmed these findings. The expression of two further
biomarkers, Dicer and MITF, was quantified with immunohistochemistry. Dicer and MITF
levels were found to be significantly down-regulated in melanoma compared to naevi.
A panel of diagnostic biomarkers was identified in this study. Having a panel enhances the
specificity of melanoma diagnosis, since it is unlikely that this exact panel will have the same
expression profile in another cancer. If together they suggest melanoma, it strengthens the
reliability of the diagnosis. ROC analysis on the biomarkers revealed high sensitivity and
specificity of the markers in distinguishing naevi from melanomas, lowering the risk of false
positives and negatives.
On evaluation of the limitations of this study, it should be noted that the sample sizes were
relatively small, and the cases themselves were selected using an exclusion criteria which
makes them less representative of the general population. There is a potential risk of false
discovery in this study due to multiple testing, however it is minimised by the presence of
two separate cohorts which produced the same findings.
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Two of the biomarkers were only able to distinguish between thick melanoma and naevi
lesions. Because the majority of lesions encountered in clinical practice are thin, their
potential is limited. However it is possible that the difference in expression between thin and
thick lesions may be due to experimental factors, such as manual micro-dissection of tissue
samples. Manual microdissection is very much dependent on technique and accuracy by
hand. Any imprecise micro-dissection may have meant that non-tumour areas, such as
keratinocytes, were micro-dissected alongside tumour cells. The risk of error in manual
micro-dissection was probably greater for thin melanoma lesions, due to the already small
amount of tumour within the section, which may account for the difference in expression. In
future, this technique can be improved with Laser capture micro-dissection (LCM), which is
more precise and contact-free.
In the future, these biomarkers require testing on a much larger cohort, consisting of
hundred to thousand naevi and melanoma samples. Once these biomarkers have been
shown to confidently distinguish between naevi and melanomas, further work in the area
would require testing on the rare ambiguous lesions. I appreciate that ambiguous lesions are
not readily available and difficult to obtain for experimental purposes. However it is
important to include them in the cohorts as the ultimate goal of this research is accurately
diagnose ambiguous lesions, which have histologically conflicting features and their
malignant potential is uncertain. Examples of ambiguous lesions include spitzoid melanoma
and nevoid melanoma.
Within clinical practice, expression levels of these biomarkers could potentially aid
diagnostic uncertainty, and be used in conjunction with histological examination. Such
molecular testing can assist histopathologists in future to make a more definitive diagnosis,
reduce misdiagnoses, and minimise the risk of litigation.
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