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. Riya Verma 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. Riya Verma 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- Riya Verma 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. Riya Verma 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 Riya Verma 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). Riya Verma 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. Riya Verma 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: Riya Verma 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. Riya Verma 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 Riya Verma 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). Riya Verma 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. Riya Verma The findings from the validation set confirmed the up-regulation of miR-22, 21 and 135b (p<0.0001). Riya Verma Riya Verma 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. Riya Verma 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. Riya Verma 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.