11. Discusión
11.3. Autoridad, autoritarismo docente y sus efectos en la convivencia escolar
Usually when investigating candidate loci in a panel of primary tumours low levels of methylation or a mixed pattern of methylation are seen. In this case a high level of methylation was seen in the majority of samples, with a small proportion of samples showing much lower methylation. Therefore it was hypothesised that the tumours with a low level of methylation could be losing methylation rather than the other samples gaining it. In an attempt to address this question the methylation of eight PBMCs, 4 nOSE, 4 normal adjacent, 4 samples which had shown low methylation, 4 samples of normal adjacent to the samples that had shown low methylation and the entire collection of primary tumours was compared and the results are shown in Figure 39 below;
Figure 39. Methylation of SP5 in various ovarian tissue by pyrosequencing. N blood whole male blood peripheral blood mononuclear cell, n OSE normal ovarian surface epithelium, NA normal adjacent, EOC epithelial ovarian cancer, LowMeth NA normal adjacent next to samples that had shown low methylation, LowMethEOC tumour samples that had shown less than 50% methylation. Each bar represent the average across all CpGs for samples of the same type. (N=2).
The highest percentage methylation was seen when the whole collection of EOC‟s were taken together. A significant difference was observed in the methylation between this
group and both the tissue adjacent to the tumours which had shown low methylation (student t-test p=4.8x10-11) and the tumours themselves which had shown low methylation (student t-test p=8.8x10-15). In addition the decrease in methylation between the tissue which was adjacent to the tumours that had shown low methylation and the low methylation tumours themselves was significant (student t-test p=0.03). This could suggest that methylation maybe first lost in the adjacent normal with a further loss of methylation in a distinct group of tumours, although the numbers investigated are clearly small. This pattern could indicate the role of methylation in maintaining tissue specific methylation patterns.
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4.6 Discussion
Eight potentially interesting candidates were identified from this experiment and the work of others with relation to these will now briefly be summarised.
CRABP1 is cellular retinoic acid binding protein. This gene encodes a specific binding
protein for a vitamin A family member and is thought to play an important role in retinoic acid mediated differentiation and proliferation. It is located on chromosome 15 and is
highly conserved among species (www.genecards.org/cgi-
bin/carddisp.pl?gene=CRABP1). SAGE databases demonstrate it is expressed in the brain, retina and spinal cord and also in skin, breast and ovarian cancers although to a lesser extent (www.genecards.org/cgi-bin/carddisp.pl?gene=CRABP1). It has been shown to be a frequently methylated tumour suppressor gene in oesophageal cancer, colorectal cancer, ovarian cancer and thyroid cancer (Huang, de la Chapelle et al. 2003; Lind, Kleivi et al. 2006; Ogino, Brahmandam et al. 2006; Tanaka, Imoto et al. 2007; Wu, Lothe et al. 2007).
AGBL2 is ATP/GTP binding protein-like 2. It is located on chromosome 11 and is thought
to play a role in the processing of tubulin (which could be of relevance given paclitaxel is one of the mainstays of ovarian cancer chemotherapy). It is expressed in a wide range of normal and cancer tissues according to the Genenote data base and expression is seen in a wide range of normal tissues using the eNorthern and SAGE databases (www.genecards.org/cgi-bin/carddisp.pl?gene=AGBL2). Characterisation of this gene, in normal tissue or cancer, or whether it is epigenetically regulated has not previously been reported.
HRASLS3, HRAS-like suppressor 3 is a tumour suppressor gene that may be involved in
interferon-dependent cell death. It is located in chromosome 11 and shows conservation from the mouse to humans. It is expressed in a variety of normal tissues and cancers according to the Genenote, eNorthern and SAGE databases (www.genecards.org/cgi- bin/carddisp.pl?gene=HRASLS3). Two authors have proposed its role as a tumour suppressor in ovarian cancer although whether gene transcription is epigenetically regulated has not been examined (Sers, Husmann et al. 2002; Nazarenko, Schafer et al. 2007).
GLS2 encodes a protein which is important in the regulation of glutamine metabolism. It is
located on chromosome 12 and highly conserved among species. It is expressed in a wide variety of tissues according to the Genenote database and a wide range of cancers according to the eNorthern data set (www.genecards.org/cgi-bin/carddisp.pl?gene=GLS2). There are a small number of publications on this gene. It is thought to be a target of p53 (Hu, Zhang et al. 2010) and when glioma cells were transfected using cDNA, reduced survival, migration and proliferation was observed (Szeliga, Sidoryk et al. 2005). It could therefore be hypothesised that it is a tumour suppressor gene that might be epigenetically regulated.
NTN4 belongs to a family of proteins related to laminins and is thought to play an
important role in neural, kidney and vascular development. It is located on chromosome 12 and conserved from mouse to humans. Expression has been noted in a wide variety of tissues and also in liver, pancreatic, breast, thyroid and ovarian cancer cell lines, according
to the eNorthern and SAGE databases (www.genecards.org/cgi-
bin/carddisp.pl?gene=NTN4). It has not been reported, to date, to have a role in ovarian cancer, or to be epigenetically regulated although recently a paper was published by Nacht et al describing its role in inhibiting angiogenesis (Nacht, St Martin et al. 2009).
SP5 is a transcriptional activator, located on chromosome 2, which has a role in the
coordination of changes in transcription required to generate the developmental pattern in the developing embryo. Using the SAGE database it is shown to be expressed in the brain colon, pancreas, prostate and placenta but expression has not been demonstrated in any cancers. It is highly conserved among species and has two CGIs (www.genecards.org/cgi- bin/carddisp.pl?gene=SP5). The primers in this thesis amplified an area in the larger CGI which is located within the promoter.
SP5 is known to be a transcription factor which antagonises SP1 (Harrison, Houzelstein et
al. 2000) and is a downstream target of Wnt signalling (Takahashi, Nakamura et al. 2005; Weidinger, Thorpe et al. 2005; Chen, Guo et al. 2006; Fujimura, Vacik et al. 2007). As Wnt is known to be dysregulated in EOC and has also been implicated in the pathogenesis of tumour initiating or sustaining cells this makes loss of methylation in tumours which regain expression of SP5 an important novel observation. We have recently shown that methylation of key genes in the Wnt pathway has an impact on PFS on ovarian cancer (Dai, Teodoridis et al. 2010) and this adds further weight to the notion that methylation plays an important role in ovarian cancer drug resistance.
SP5 has been shown to be dynamically expressed during CNS development (Harrison,
Houzelstein et al. 2000; Treichel, Becker et al. 2001; Weidinger, Thorpe et al. 2005) but it was only recently noted to show increased expression in colorectal, gastric and hepatocellular cancers with a negative impact (Chen, Guo et al. 2006). Chen et al, using an inducible gene expression system combined with microarray analysis found that over expression of SP5 in MCF7 cells resulted in significant growth promotion supporting our results (Chen, Guo et al. 2006). This fits with the high levels of methylation seen in the cell lines and majority of primary tumours. The authors also identified downstream targets of SP5 in the microarray experiment; many of these genes have been implicated in ovarian cancer and drug resistance and some have been shown to have epigenetic modulation of their gene expression – they include p21, TGFB1, MDM2, ABCG2 and ABCC3 (see Chapter 1.3).
4.7 Conclusion
In these experiments the aim was to validate MLDA as a statistical technique capable of identifying sequences that gain methylation in resistant cell lines by DMH (Dai, Teodoridis et al. 2008). The hypothesis being that such loci could represent candidate biomarkers of acquired platinum resistance. It should be noted though that for this reason it was not the top ranking sequences that were analysed but instead random sequences, which ranked from 2 to 75. Eight of nine sequences showed increased methylation in the A2780- derived resistant cell lines and we can therefore concluded that MLDA is at least as good at identifying candidate loci as PAM or SAM.
Methylation of SP5 was not associated with response to chemotherapy or PFS or OS. Given that only a small number of tumours showed a decrease in methylation it is possible
that both the response and survival analysis were underpowered. However analysis of the matched pairs also showed no significant differences.
However it remains possible that a decrease in methylation at SP5 could correlate with an increase in expression and have functional significance – given the other publications relating to this gene. If this is the case then the DMH experiment may have highlighted an important gene (SP5) or pathway (Wnt) in ovarian cancer.
In conclusion, in this chapter we demonstrated that MLDA (Dai, Teodoridis et al. 2008), a novel statistical technique developed in our laboratory, was able to identify loci which showed differential methylation between sensitive and resistant A2780 based cell lines. Given that loci were chosen at random to be characterised and ranked up to 75th (by MLDA ranking) it was encouraging that all but one locus validated in the same cell line panel.
For SP5 we hypothesised we would see an increase in methylation in the resistant cell lines and a heterogeneous pattern of methylation in the primary tumours and although SP5 did not show this pattern and instead showed a very high level of methylation in nearly all cell lines and primary tumours, it was thought that this in itself was a interesting observation.
In addition it appeared that rather than sequences gaining methylation that it may be a small cohort of samples that were losing methylation (which could be causing an increase in gene expression) - and a negative phenotypic effect from over expression of this gene would fit with the published work of others, as outlined above. As a result we decided that further more functional experiments were warranted and these are described in Chapter 6. Other candidates were excluded from further analysis due to the low levels of methylation seen in primary tumours, but as was discussed in the last chapter this may be flawed logic.
There was concern though that the changes that are seen in the A2780 resistant series were not seen in the in vivo generated resistant cell lines and even sometimes showed the opposite, for example in the case of CRABP1 where a decrease in methylation was seen between PEO14 and PEO23. Further refinement in the approach could be achieved by identifying candidate loci from methylation changes seen between in vivo generated sensitive and resistant cell lines (PEA1 and PEA2, PEO14 and PEO23) as opposed to in
vitro generated cell lines (A2780 sensitive lines, A2780 cp70 and MCP 1-9), by DMH.
5 Characterisation of loci showing differential
methylation between patient derived cell line
pairs by MLDA
The experiments described in this chapter aimed to investigate whether using sensitive and resistant cell lines, where the resistance was generated within patients during chemotherapy, identified more clinically relevant methylation biomarkers of acquired drug resistance in ovarian cancer. In order to address this, PEA1 (sensitive) vs. PEA2 (resistant) and PEO14 (sensitive) vs. PE023 (resistant) were used instead of the A2780 panel. These cell lines were generated from patients with epithelial ovarian cancer who had been treated with platinum and subsequently developed resistance, and are referred to in the text as the
in vivo cell lines (Langdon, Lawrie et al. 1988).
Loci which showed increased methylation in the resistant in vivo ovarian cancer cell lines were identified using MLDA. Figure 40a below shows how candidate loci are identified. Those of interest are seen as outliers on this line. The six loci we were particularly interested in were those that gained maximal methylation in the resistant pair and these are shown in figure 40b within the red box.
Figure 40a. MLDA: Sensitive and resistant scores for 6 loci which gain methylation in resistant cell lines. The top two diagrams show the score in the sensitive cell line PEO14 and the resistant cell line PEO23. The bottom two diagrams show the same for the PEA1 and PEA2 pairing. Coloured crosses highlight the position of the 6 loci within the whole data set and they can be seen to move from an area associated with less methylation to one of more methylation, between the sensitive and resistant cell lines (for a more complete explanation of how these figures are derived please refer to Dai et al, Figure 3 and Figure 7). (Dai, Teodoridis et al. 2008).
Figure 40b. Identification of hypo- and hyper- methylated outliers by MLDA. Loci which lose methylation in the resistant cell lines are shown as non filled circles within green box. Loci which gain methylation in resistant cell lines are shown as non filled circles within the red box.
Of the 6 sequences which showed increased methylation in the two resistant cell lines by MLDA score, one locus did not contain a CGI and another had more than more BLAT (Blast like alignment tool) alignment and we therefore selected 4 sequences to characterise in more detail in this chapter. These are shown in table 29 below. We did not choose to characterise the sequences that showed a decrease in methylation in the resistant cell lines. This was a pragmatic decision given that the characterisation of each locus is time consuming. We hypothesised that a gain in methylation in the resistant cell lines would be more functionally relevant and have more potential as a biomarker than a loss in methylation.
Table 29 List of sequences gaining methylation in PEA2 and PEO23 (resistant) cell lines.
MLDA Rank Microarray Identifier1 Gene Symbol
CGI2 PEO14 PEO23 PEA1 PEA2 Residual
score 1* Residual score 2* 1 85B2 LOC113230 Y -1 -0.2 -1 0.45 0.8 1.45 2 21G5 KIAA1383 Y -1 0 -1 0.2 1 1.2 3 17G11 SIX1 Y -1 0.45 -1 1 1.45 2 4 66G6 - Y -0.45 1 -1 0 1.45 1.0001 1
http://data.microarrays.ca/cpg/searchsingleclones.htm. 2CGI Gardener Garden Y yes, N no. *The residual score is the difference of MLDA score between pair of cell lines (1: PEO14 vs. PEO23; 2: PEA1 vs. PEA2), and positive residual score indicates increased methylation in the resistant cell line. MLDA score (see MLDA paper Figure 3) (Dai, Teodoridis et al. 2008) representing how consistently the locus was methylated
(positive score) or unmethylated (negative score) in duplicates.