Pro-neural transcription factors as cancer markers
- Maria Vias1,
- Charlie E Massie1,
- Philip East2,
- Helen Scott1,
- Anne Warren3,
- Zongxiang Zhou4,
- Alexander Yu Nikitin4,
- David E Neal†1 and
- Ian G Mills†1Email author
© Vias et al; licensee BioMed Central Ltd. 2008
Received: 17 December 2007
Accepted: 19 May 2008
Published: 19 May 2008
The aberrant transcription in cancer of genes normally associated with embryonic tissue differentiation at various organ sites may be a hallmark of tumour progression. For example, neuroendocrine differentiation is found more commonly in cancers destined to progress, including prostate and lung. We sought to identify proteins which are involved in neuroendocrine differentiation and differentially expressed in aggressive/metastatic tumours.
Expression arrays were used to identify up-regulated transcripts in a neuroendocrine (NE) transgenic mouse model of prostate cancer. Amongst these were several genes normally expressed in neural tissues, including the pro-neural transcription factors Ascl1 and Hes6. Using quantitative RT-PCR and immuno-histochemistry we showed that these same genes were highly expressed in castrate resistant, metastatic LNCaP cell-lines. Finally we performed a meta-analysis on expression array datasets from human clinical material. The expression of these pro-neural transcripts effectively segregates metastatic from localised prostate cancer and benign tissue as well as sub-clustering a variety of other human cancers.
By focussing on transcription factors known to drive normal tissue development and comparing expression signatures for normal and malignant mouse tissues we have identified two transcription factors, Ascl1 and Hes6, which appear effective markers for an aggressive phenotype in all prostate models and tissues examined. We suggest that the aberrant initiation of differentiation programs may confer a selective advantage on cells in all contexts and this approach to identify biomarkers therefore has the potential to uncover proteins equally applicable to pre-clinical and clinical cancer biology.
In recent years there has been much effort to identify new prostate cancer biomarkers. Malignant prostatic tumours commonly contain scattered or focal neuroendocrine type cells, but only a small minority or prostate cancers contain an homogenous population of such cells, when they are classified as small cell prostatic carcinoma. However, other regular prostate carcinomas which have an increased NE phenotype are at increased risk of tumour progression and castration resistance [1–3]. We recently reported that long-term anti-androgen treatment induces NE differentiation in a cell line model, giving rise to a more invasive phenotype .
Some previous studies have failed to find convincing correlations between focal NE differentiation and prostate cancer progression [5–7] Variations in expression and detection of neuron-specific enolase, chromogranin A and synaptophysin may be partly responsible for this controversy. Therefore better markers for a neural or neuroendocrine phenotype would benefit the field.
Multiple basic helix-loop-helix (bHLH) proteins play a critical role in the regulation of neural stem cell differentiation . The bHLH family of transcription factors includes activators and repressors of transcription. The activator-type bHLH transcription factors include 'achaete-scute complex' homologue 1 (Ascl1) which is expressed in differentiating neurons and belongs to the Neurogenin Family. This activating bHLH transcription factor is believed to drive the expression of a 'hairy and enhancer of split' factor, Hes6. Hes6 in turn can support Ascl1 activity and neuronal differentiation in part by antagonising Hes1 activity through heterodimer formation . Hes1 is a repressor-type bHLH transcription factor which maintains neural stem cells by repressing activator bHLH expression . In the case of Hes1 this occurs at two levels: firstly through direct binding to the Ascl1 promoter, and secondly by forming a non-functional heterodimer with another activator-type bHLH transcription factor, E47 [9, 11]. Overall, Hes proteins are involved in the maintenance of neural stem cells and gliogenesis, whilst Ascl1 is implicated in neurogenesis [12–14].
In silico approaches
Expression array data from p53 PE-/-; Rb PE-/- cancerous (n = 5) and normal (n = 3) samples were retrieved from a previously published data set . Gene expression data from the p53 PE-/-; Rb PE-/- mouse model expression array data were analysed in the R statistical software using the limma and affy packages [16, 17]. Briefly, data were pre-processed using the RMA (Robust Multichip Average) method, before fitting a linear model and applying Bayesian smoothing to identify differentially expressed genes between the normal and cancer samples. M-values (log2 expression ratios) were calculated for all probes and for each sample and then complete hierarchical clustering was performed using the Eisen Cluster program . Heatmaps were generated using the Eisen TreeView program.
Median centred log2 ratios of normal adult tissue transcript levels were retrieved from the Oncogenomics Normal Tissue Database  for genes which were found to be differentially regulated in the p53 PE-/-; Rb PE-/- mouse model of prostate cancer using IMAGE clone identifiers retrieved from the Clone/Gene ID converter [20, 21].
Clinical prostate cancer expression array data were retrieved from the NCBI Gene Expression Omnibus (accession numbers GSE3325 and GSE6099) from a previously published Affymetrix expression array data set. To generate dot plots, data were pre-processed using the RMA (Robust Multichip Average) method, quantile normalised and intensity estimate values were averaged for all probes for a given gene.
Statistical significance (p values) for a panel of neuroendocrine markers in human tumours.
BN vs PCa
BN vs MET
PCa vs MET
Statistical Analysis of the ExpO Dataset
Each chips MAS5 generated signal intensity estimates were scaled to the chip median. We acknowledge a quantification algorithm such as RMA would be a preferable step here but due to the large size of the data set and available computational resources this proved problematic. The normalised expression vectors and sample annotation were databased using MySQL to allow efficient access to the expression profiles . The DBI package within R was used to extract neuroendocrine gene profiles (205311_at:DDC, 206291_at:NTS, 206940_s_at:POU4F1, 209985_s_at:ASCL1, 209987_s_at:ASCL1, 209988_s_at:ASCL1, 211341_at:POU4F1, 213768_s_at:ASCL1, 214347_s_at:DDC, 226446_at:HES6, 228169_s_at:HES6, 204697_s_at:CHGA). Each gene profile was scaled to its median value across all samples and a Bioconductor Expression Set object created. The profiles were grouped into functional gene sets and a median signal intensity calculated per sample (Hes6, Hes6-Chga, Hes6-Ddc, Ascl1, Ascl1-Hes6-Nts-Ddc). Samples displaying a greater than 3 fold induction were selected. Identified samples were then tested for enrichment of tumour type using a hyper-geometric distribution. (R code provided below). The function phyper.expo(decide, pdat, th = 2) is called with a vector (decide) indicating up regulated, down regulated and unselected genes (1, -1, and 0 respectively), a dataframe (pdat) containing tumour type labels etc. as columns and a threshold (tr) setting the minimum number of tumour type hits within the selected samples to report. A list is returned containing tumour label, the number of tumour type samples within the sample set (hits), the number of tumour type samples within the ExpO dataset,(foreground), the number of non tumour type samples within the ExpO dataset (background), the number of samples selected on expression (sample.size) and the p-value (p. value) associated with enrichment.
Quantitative real time polymerase chain reaction (qRT-PCR)
Primer sequences for quantitative real-time PCR validation.
Small interference RNA silencing
Cells were washed with PBS, trypsinized and centrifuged at 1300 rpm for 3 minutes. The cell pellet was resuspended and cells were counted using the haemocytometer. 2 × 106 cells were mixed with 100 μl of Nucleofector solution R (Amaxa, GmbH) and 1 μg of siRNA duplexes was added. Using the Nucleofector program T-09, a specific electrical current is applied to the cells and the DNA is delivered into the nucleus. Cells were transferred to culture dishes containing RPMI media supplemented with 20% FBS to facilitate cell attachment. Media was changed 12 hours after transfection and RNA was extracted from cells after 24 hours. Hes6 siRNA duplexes were purchased from Dharmacon (Lafayette, CO). Hes6 siRNA was designed against the human mRNA of Hes6 (GenBank accession number NM_018645) and consists of two selected siRNA duplexes. The target sequence for the duplex 1 was CAGCCTGACCACAGCCCAA (sense: CAGCCUGACCACAGCCCAAUU; antisense: 5'-P UUGGGCUGUGGUCAGGCUGUU) whereas the target sequence for duplex 2 was AAGCTTGAACTTGCCACTTCA (sense: r(GCUUGAACUUGCCACUUCA)dTT; antisense sequence: r(UGAAGUGGCAAGUUCAAGC)dTdT).
A database encompassing patients undergoing radical prostatectomy at Addenbrookes Hospital, Cambridge was interrogated. Sections containing regions of normal, BPH, PIN and cancer were classified by uro-pathologists within specimens taken from 32 patients. The cancerous regions were further sub-divided into Gleason Grade 3,4 and 5. Duplicate cores were taken from within each section to provide material corresponding to each Gleason Grade as well as BPH, PIN and Normal material for each patient. This material was arrayed on a tissue microarray (TMA). Material was collected with full ethics approval from a local ethics committee (MREC/01/4/061 and LREC 02/281 M) and as part of the ProMPT Study: Molecular mechanisms and the development of novel treatment strategies in progressing prostate cancer – Northern (and Bristol) Prostate Cancer Collaborative).
Mouse materials were collected and processed as described earlier . This was conducted in compliance with international guidelines as confirmed in the original paper describing the model.
Paraffin blocks from identified patients that had been selected for construction of a tissue micro-array (TMA) were cut using a standard microtome at 5 μm thickness and stained with hematoxylin and eosin (H&E). Confirmation of tissue status (Gleason grades and BPH) was conducted by an uro-pathologist, who assessed and marked the blocks appropriately. 0.6 mm tissue cores were cut and constructed according to pre-determined layout.
For NT (Sigma), NTR2 (Acris Antibodies GmbH) and Ascl1 (Aviva Systems Biology) antibodies, antigen retrieval was performed in Tris-EDTA at pH 9.0 in a microwave for 15 minutes. Blocking was performed using 1% donkey serum in PBS for 1 hour. Primary antibody dilutions were as follows: NT and Ascl1 were used at 1:100 and NTR2 at 1:500. Staining was performed using the same blocking solution at 4°C overnight. After three washes in PBS, a biotin-SP-AffiniPure donkey anti-rabbit secondary antibody was applied at a dilution of 1:200 for 45 minutes. Visualization was achieved using an VECTASTAIN Elite ABC kit (Vector Laboratories) for 45 minutes and colour was accomplished using 3,3'-diaminobenzidine (DAB) for 1 minute.
Following immunostaining protocols, the TMAs were assessed to determine the degree of TMA core loss or disruption, which varied between the different TMAs. In those cores that remained intact, immunostaining was evaluated according to staining intensity. Scoring was performed independently by two observers (one an independent specialist uro-oncology pathologist) both blinded to the TMA plan. Staining intensity for Ascl1, NTR2 and NT were scored on a scale of 0–5, where 0 means no staining, 1 means minimal staining and 5 means maximum intensity. For clarity of presentation, the staining was then classified into negative (0), low (1–2), medium (3), high (4) and very high (5) intensity. The two assessors compared scores and a consensus agreement was reached on the staining intensity of each core.
Results and Discussion
The implication of the expression signature from p53 PE-/-; Rb PE-/- mouse tumours is that a transdifferentiation program has been initiated by knocking out p53 and Rb. This is reflected in the expression of neuroendocrine markers and the change in levels of activator-type and repressor-type bHLH transcription factors. Clearly this does not produce a pure neural lineage in the tumours since they continued to express the androgen receptor together with cytokeratin 5, a basal cell marker, and cytokeratin 8, a luminal epithelial cell marker .
Once again we were able to rule out derepression of differentiation signals as a contributory factor to the neural phenotype (e.g., through loss of Hes1 expression). Hes1 transcript levels remained unchanged in the clinical material in agreement with our observations in the LNCaP cell-line (Figure 6A). A member of the NeuroD family of pro-neural transcription factors NeuroD1 was also up-regulated in the metastatic samples (Figure 6A). The neurogenin family of pro-neural transcription factors was also studied. Neurogenin 1 (Ngn1) did not significantly change, neurogenin 3 (Ngn3) slightly increased in metastatic tissue, and neurogenin 2 (Ngn2) was also up-regulated in this tissue (P = 0.03) (Figure 6A). Some of the targets we have previously identified as up-regulated after long-term treatment with bicalutamide were also assessed . Of those, synaptotagmin 4 (Syt4) and aspartate beta-hydroxylase (Asph) were increased in the transition from localised prostate cancer to metastatic tumours with p-values of 0.006 and 0.001 respectively, whereas Atp11a was highly increased in prostate cancer (P = 0.005) (Figure 6A). When a cluster analysis of the same data set was performed, it was possible to discriminate between metastatic and benign and primary tumours by using Ascl1, Hes6, Ddc and Nts as marker genes (Figure 6B).
Combinations of neuroendocrine biomarkers are enriched in specific malignant tissue types.
In conclusion, distinct prostatic tumour models and material (cell-lines derived from human tumours, transgenic mouse tumours and patient samples) all display the hallmarks of neural transdifferentiation during the progression to metastatic disease which was associated with a change in the balance of activity and expression in favour of activator-type bHLH transcription factors including Hes6 and Ascl1. Similar changes are discernible in subgroups of tumours at other sites. Collectively this suggests that impairing the activation of pro-neural transcription factors may pay dividends in cancer treatment. However, transcription factors are not conventionally druggable. Nonetheless, antisense oligonucleotide therapy has recently entered phaseII clinical trials to target a chaperone protein, clusterin . Suicide gene therapy has also been proposed in which a therapeutic gene, for example Herpes Simplex Thymidine Kinase or E. Coli purine nucleoside phosphorylase, under the control of promoters for transcription factors exclusively over-expressed in cancer cells such as Ascl1 is expressed and activates Ganciclovir to induce cell cycle arrest [34–36]. In addition 'stapled' peptides are being developed to disrupt protein-protein interactions which may become relevant for targeting transcriptional complexes as well . In light of this study and in combination with these new technologies we may, in future, be capable of exploiting transcription factor activity to control cell fate and improve patient survival.
List of Abbreviations
Achaete-scute complex homologue 1
benign prostatic hyperplasia
glyceraldehyde 3-phosphate dehydrogenase
Hairy Enhancer of Split
Neurotensin Receptor 2
prostatic intraepithelial neoplasia
real-time polymerase chain reaction
TATA binding protein
This work was supported by NIH grants CA96823 and RR017595 and Department of Defense grant PC010342 to A.Y. N. M. V. is supported by a ProMPT/MRC PhD studentship. C.E.M. is supported by a CRUK Programme Grant. H.E.S. and I.G.M. are supported by core CRUK funding. We would like to acknowledge the wider support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. The authors would like to acknowledge the support of the National Cancer Research Institute (NCRI) formed by the Department of Health, the Medical Research Council and Cancer Research UK. The NCRI provided funding through ProMPT (Prostate Mechanisms of Progression and Treatment) and this support is gratefully acknowledged. We would like to thank Joana Borlido for critical reading of the manuscript and helpful input.
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