Identification of potential therapeutic targets in prostate cancer through a cross‐species approach

Abstract Genetically engineered mouse models of cancer can be used to filter genome‐wide expression datasets generated from human tumours and to identify gene expression alterations that are functionally important to cancer development and progression. In this study, we have generated RNAseq data from tumours arising in two established mouse models of prostate cancer, PB‐Cre/PtenloxP/loxP and p53loxP/lox PRbloxP/loxP, and integrated this with published human prostate cancer expression data to pinpoint cancer‐associated gene expression changes that are conserved between the two species. To identify potential therapeutic targets, we then filtered this information for genes that are either known or predicted to be druggable. Using this approach, we revealed a functional role for the kinase MELK as a driver and potential therapeutic target in prostate cancer. We found that MELK expression was required for cell survival, affected the expression of genes associated with prostate cancer progression and was associated with biochemical recurrence.

2. It is not clear why the authors chose to focus on the RNAseq from the Pten-/-mice and not the p53-/-;Rb-/-mice in Figure 2. A better justification for this in the text is needed. a. Note: labeling of AdT-specific genes in unclear in Fig. 2E 3. The two distinct mouse models are not clearly labeled in Supplemental Fig. 4, and an image of PIN lesions for the p53-/-;Rb-/-model as described in the text is lacking.
4. Could the authors comment on the age to X phenotype (PIN, MedTumor, AdTumor) for each of the mouse models? Although these are well-studied models, this information would allow the reader to better place this study in the context of the field. Fig. 6E is unclear. It is difficult to tell which line represents MELKi 4, and there are unlabeled data points as well.

The labeling of Supplemental
Referee #3 (Comments on Novelty/Model System for Author): The authors do a good job of describing the differences between the mouse and human prostate and the two different genetically engineered mouse models they use in their studies.
Referee #3 (Remarks for Author): The manuscript by Ramos-Montoya, et al., is well written with a sound experimental approach to identify novel therapeutic targets in prostate cancer (PCa). Their approach started with rational comparisons between two transgenic mouse models of PCa (PB-Cre/p53PRb and PB-Cre/PTEN), including differences of tumor grade and location within the mouse prostate where tumors form. The authors also nicely describe anatomical differences between mice & human prostates as a rationale for including tumors from multiple regions of the mouse prostate in each of the model systems for further analysis. Using RNAseq expression profiling and a set of bioinformatics-based filtering steps to reduce the complexity of the differentially expressed genes and to help identify changes that are likely to be therapeutically targetable, the authors found MELK to be significantly associated with more aggressive tumors and poor outcome in human patients. The authors then go on to show, in several human cell line models of PCa, that abrogating MELK expression using siRNA or adding a putative inhibitor of MELK (MELKi) activity inhibits cellular proliferation and induces apoptosis and that treating mice harboring cell line xenografts with the MELKi slowed tumor growth and increased apoptosis within the tumors.
The work identifying MELK as a potential therapeutic target in PCa is very thorough and the results and interpretations are sound. However, the studies conducted to validate MELK as a therapeutic target in PCa rely heavily on the activity and specificity of the chosen MELKi (OTSSP167). Although many of the results obtained using the MELKi were similar to the results obtained with MELK-targeting siRNA, this correlation is insufficient support that the putative MELKi is, in fact, inhibiting MELK activity. Moreover, the authors do not describe the source of the MELKi nor do they provide any supporting evidence of its specificity toward MELK. In fact, the authors themselves acknowledge the recent report that seems to invalidate the presumed dependency on MELK of several cell line models (https://elifesciences.org/articles/24179 ) and suggests the antiproliferative activity of OTSSP167 has substantial effects on targets other than MELK. Thus, the inference that MELK regulates mitotic spindle formation is not sufficiently proven since OTSSP167-treated cells were used to draw this conclusion.
Due to the uncertain specificity of OTSSP167 toward MELK, several questions should be considered in order to bolster the authors' findings of the importance of MELK in PCa aggressiveness and its utility as a therapeutic target. 1) Does the converse experiment (overexpression of MELK in a low-expressing cell line vs abrogation of expression is a high-expressing cell line) result in the upregulation of the same genes and increase aggressiveness? 2) Does the expression of genes identified as MELK-regulated get modulated similarly in response to OTSSP167 treatment of cells that do not express MELK? 3) Does decreasing cellular proliferation by another means, e.g. androgen deprivation of androgen-dependent CaP cells, result in similar expression modulation of the same genes? 4) Are there any CaP cell lines that do not respond to MELKi or siMELK? A new paragraph has been included in the discussion section discussing these recent publications and commenting on the possible role that MELK inhibition could play in the context of prostate cancer progression towards antiandrogen therapy resistance due to cellular lineage plasticity, neuroendocrine and stem cell phenotype acquisition.

Reviewer's comment:
It is not clear why the authors chose to focus on the RNAseq from the Pten-/-mice and not the p53-/-;Rb-/-mice in Figure 2. A better justification for this in the text is needed.

Authors' response:
The reason that Figure panels 2C to 2E focus on the Pten-/-data is that the analyses conducted would not have been feasible with the data obtained from p53-/-;Rb-/-mice. Only a very small number of differentially expressed genes were identified in PIN lesions from p53-/-;Rb-/-compared to normal prostate lobes (between 25 and 63 genes depending on the lobe, at a significance level of 0.01). We believe that this is because the p53-/-;Rb-/-mice developed low-grade PIN lesions, which did not accumulate many gene expression alterations. The analyses described in Figure panels 2C to 2E, e.g. pathway analysis, would not have been informative with such a small number of differentially expressed genes. Furthermore, we were unable to differentiate similarly distinct stages of tumour progression in the p53-/-;Rb-/-mice as we did in the Pten-/-mice (e.g. medium-and advanced-stage tumours). In our hands, the Pten-/-model was thus better suited to exploring how gene expression patterns differ between different prostate lobes and stages of prostate cancer progression, and we have now revised the manuscript to explain this.
It is worth noting that the subsequent analyses, including the cross-species analyses aimed at identifying potential therapeutic targets, considered both mouse models to an equal extent, and we found that this was valuable as it improved the overlap between the mouse and human data ( Figure EV1B).

Reviewer's comment:
Note: labeling of AdT-specific genes in unclear in Fig. 2E Authors' response: We thank the reviewer for bringing this to our attention and have corrected the labelling to be consistent with the other figures.

Reviewer's comment:
The two distinct mouse models are not clearly labeled in Supplemental Fig. 4, and an image of PIN lesions for the p53-/-;Rb-/-model as described in the text is lacking.

Authors' response:
We thank the reviewer for bringing this to our attention. Supplemental Figure 4 (now referred to as Figure EV3 in this resubmission) has been revised to include labels indicating the two distinct mouse models. The image showing PIN lesions for the the p53-/-;Rb-/-model that was inadvertently ommitted in the first version of this figure has also been added.

Reviewer's comment:
Could the authors comment on the age to X phenotype (PIN, MedTumor, AdTumor) for each of the mouse models? Although these are well-studied models, this information would allow the reader to better place this study in the context of the field.

Authors' response:
This information has been added to the beginning of the results section.

Reviewer's comment:
The labeling of Supplemental Fig. 6E is unclear. It is difficult to tell which line represents MELKi 4, and there are unlabeled data points as well.

Authors' response:
This figure (referred to as Figure EV5 in this resubmission) has been edited for clarity.

Reviewer's comment:
The work identifying MELK as a potential therapeutic target in PCa is very thorough and the results and interpretations are sound. However, the studies conducted to validate MELK as a therapeutic target in PCa rely heavily on the activity and specificity of the chosen MELKi (OTSSP167). Although many of the results obtained using the MELKi were similar to the results obtained with MELK-targeting siRNA, this correlation is insufficient support that the putative MELKi is, in fact, inhibiting MELK activity. Moreover, the authors do not describe the source of the MELKi nor do they provide any supporting evidence of its specificity toward MELK. In fact, the authors themselves acknowledge the recent report that seems to invalidate the presumed dependency on MELK of several cell line models (https://elifesciences.org/articles/24179 ) and suggests the antiproliferative activity of OTSSP167 has substantial effects on targets other than MELK. Thus, the inference that MELK regulates mitotic spindle formation is not sufficiently proven since OTSSP167-treated cells were used to draw this conclusion. We thank the reviewer for these important comments and suggestions, and we agree that the specificity of the compound used to inhibit MELK is a key consideration, considering that off-target effects are commonly observed with kinase inhibitors. In the revised manuscript as well as in this response, we are thus presenting additional data and context to support the conclusions drawn in our study. The MELK inhibitor used in this study, OTS167, was first described by Chung and colleagues (Chung et al, 2012). In their study, the ability of OTS167 to inhibit MELK was demonstrated using in vitro kinase assays. Chung and colleagues also tested in their study the growth inhibitory effect of OTS167 in several different cancer cell lines and found that cells with low MELK expression are much less sensitive to growth inhibition by OTS167 than cells with high MELK expression. We have revised Figure EV4 to include data supporting that OTS167 also inhibits MELK at the concentrations and in the experimental system used in our study. Treatment of C4-2b cells with OTS167 reduced the phosphorylation of ACC at Ser79 (revised Figure EV4A), a known MELK substrate, in a dose-dependent manner (Beullens et al, 2005). Furthermore, OTS167 treatment also reduced MELK protein levels (revised Figure S5A), which has been previously observed and attributed to decreased MELK stability due to inhibition of autophosphorylation (Lizcano et al, 2004;Badouel et al, 2010;Chung et al, 2016). Taken together, these results support the conclusion that OTS167 does indeed inhibit MELK activity under the experimental conditions used in this study. We have also added the source of OTS167 used in our experiments to the methods section, which was inadvertently omitted in the original version of this manuscript. We fully agree with the reviewer that studying the effects of MELK overexpression in a low-expressing prostate cancer cell line would be a worthwhile experimental approach, and indeed increased aggressiveness following overexpression of MELK has been previously demonstrated in breast cancer (Wang et al, 2014). However, despite our best efforts we were unable to identify a prostate cancer cell line that could serve as a suitable model system. We initially investigated five prostate cancer cell lines (LNCaP, C4-2, C4-2b, PC-3, DU145) and one nontransformed prostate cell line (PNT1a) that are regularly used in our laboratory. All six of these cell lines exhibited robust expression of MELK, with only relatively minor differences between cell lines (Response Figure 1); MELK expression levels as assessed by qPCR were less than 2-fold higher in the highest-expressing cell line (DU145) than in the lowest-expressing cell line (PNT1a). In an effort to identify a more suitable MELK low-expressing cell line, we retrieved data on MELK expression from the Cancer Cell Line Encyclopedia (https://portals.broadinstitute.org/ccle). MELK expression data was available for eight prostate cancer cell lines (NCIH660, VCaP, MDAPCA2B, DU145, LNCaP, 22RV1, PC3, PRECLH), five of which were not included in our own cell line panel. A comparison of MELK expression in prostate cancer cell lines with other cell lines for which data is available in the Cancer Cell Line Encyclopedia illustrates that MELK expression in all eight prostate cancer cells is comparatively high overall and displays relatively little variation between cell lines (Response Figure 2). In contrast to many other cancer types, e.g. breast, stomach and melanoma, there are no clear "outliers" with low MELK expression among prostate cancer cell lines that are likely to be promising models to test the effect of MELK overexpression. The reviewer also raises the question of whether there are any prostate cancer cell lines that do not respond to siMELK or treatment with OTSSP167. In our laboratory, we have so far tested the growthinhibitory effect of siMELK in LNCaP, C4-2, C4-2b and PNT1a cells, and have observed reduction of proliferation and decreased cell viability in all cases ( Figure 5D, Figure EV4E, Response Figure 3). Consistent with this, all prostate cell lines tested to date (LNCaP, C4-2, C4-2b, PC-3, DU145, PNT1a) were sentitive to treatement with OTS167. These results are not surprising, considering that all of these cell lines exhibit robust MELK expression as outlined above. Interestingly, despite the relatively modest differences in MELK expression, we did observe a statistically significant correlation between MELK expression levels and sensitivity to OTS167 in the six cell lines, which would be consistent with the interpretation that the growth inhibitory effects of OTS167 may be mediated by MELK. These results have now been incorporated into Figure 5F. Figure 3: Effect of siMELK on growth of C4-2 and PNT1a cells. C4-2 cells (left) or PNT1a cells (right) were transfected with siRNAs directed against MELK or a non-targeting control, and viable cells were counted at the time points indicated. Preliminary data -n = 2 for C4-2, n = 1 for PNT1a, with three technical replicates per biological replicate.

Response
In order to address the reviewer's question whether inhibition of cell proliferation by other means results in expression modulation of the same genes as treatment with OTS167, we used microArray and RNA-seq data of LNCaP and C4-2b cells treated with established growth-inhibitory compounds: the androgen-inhibitors enzalutamide (Wang et al, 2016) or bicalutamide (unpublished data from our laboratory) and the AMPK activators AICAR and metformin 24 h (Jurmeister et al, 2014). To facilitate cross-comparability, we only used genes covered in all datasets (11,210 genes), averaged microArray data across all probes of the same gene after inter-quartile normalisation, and used a moderated log2 fold change estimate for RNAseq data. We then used principal component analysis (PCA) to discern systemic differences between treatments. As shown in Response Figure 4, the gene expression profile of cells transfected with siMELK #2 most closely resembled that of cells treated with OTS167 for 24 h, and the gene expression profile of cells transfected with siMELK#3 most closely resembled that of cells treated with OTS167 for 8 h. By contrast, there was greater variance between MELK knock-down or OTS167 treatment and the other growth-inhibitory stimuli. This suggests that silencing of MELK and treatment of OTS167 result in relatively similar gene expression profiles compared to unrelated treatment conditions. To facilitate cross-comparability, only genes covered in all conditions (11,210 genes), were selected. microArray data was averaged across all probes of the same gene after interquartile normalisation. A moderated log2 fold change estimate was used for RNAseq data. PCA was used to discern systemic differences between treatments.
The totality of data presented above and in our revised manuscript continues to supports the conclusion that the growth-inhibitory effects of OTS167 in prostate cancer cells are at least in part mediated through MELK: • OTS167 inhibits MELK under the experimental conditions used in the study, as evidenced by reduced phosphorylation of a MELK substrate and decreased MELK protein levels. • MELK expression positively correlates with sensitivity to OTS167 in a panel of prostate cell lines. • Treatment with OTS167 and silencing of MELK both result in similar changes in the expression of cancer-relevant genes, and the resulting gene expression profile is distinct from that induced by unrelated growth-inhibitory compounds. • Growth inhibition and induction of apoptosis are not only observed following treatment with OTS167, but also following siRNA-mediated knock-down of MELK. Nonetheless, we have revised the discussion section of the manuscript in order to accurately reflect recent literature indicating that, like most kinase inhibitors, OTS167 inhibits more than one kinase (Ji et al, 2016), and to discuss the potential implications for our study. To avoid giving the impression of complete specificity for MELK in absence of data to this effect, we have also changed all references to the inhibitor in the text and figures from "MELKi" to "OTS167".
Finally, we acknowledge the reviewer's point that further experiments will be required in order to determine whether the effect of OTS167 on mitotic spindle formation is mediated through MELK or through another target of the inhibitor, and we have now revised the text and figures of our manuscript to reflect this. Nevertheless, we feel that this does not significantly impact the main conclusions of the study, namely that the cross-species approach described in the manuscript is able to identify potential therapeutic targets in prostate cancer, of which MELK serves as one example.

Reviewer's comment:
A graphical overview of the approach used to identify MELK as a target for PCa would be useful.

Authors' response:
We agree with the reviewer and have revised Figure 3C to show a graphical overview of the steps used to derive potential therapeutic target genes for prostate cancer and identify MELK.

Reviewer's comment: Experimental methods section (and other places in the main text) should refer to Supplementary Text for relevant information. Authors' response:
We have revised the main text in line with the reviewer's suggestion.

Reviewer's comment: White areas in legends of Fig 4D & E are not represented in graph, challenging rapid interpretation. Authors' response:
We have revised the legend of Figure 4D and E and hope that this will aid interpretation of the figure. Thank you for the submission of your revised manuscript to EMBO Molecular Medicine. We have now received the enclosed reports from the referees that were asked to re-assess it. As you will see the reviewers are now supportive and I am pleased to inform you that we will be able to accept your manuscript pending a few final editorial amendments.
Referee #3 (Comments on Novelty/Model System for Author): As the title indicates, this approach identifies *potential* therapeutic targets, making these studies largely preclinical. Medical impact will be higher when therapeutic targets identified by this approach are validated in human trials.
Referee #3 (Remarks for Author): The authors have addressed the reviewers' concerns very thoughtfully and thoroughly.

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See Materials and Methods section, "Data Analysis and Graphical Representation". No power analysis was done a priori of study design, since the effect size in changes was unknown. Generally a minimum of n=3 biological replicates was used. See Appendix Supplementary Methods, "In vivo studies". We did not perform any statistical method to choose the group size of the in vivo studies, as we did not have enough information on the variability of the model being used. For that reason we chose to use an n=10 for each animal group, expecting that such size would provide enough power to the study to be able to detect the effects induced by the treatments. See Appendix Supplementary Methods, "In vivo studies". No blinding was done in the in vivo studies. However, for the calliper measurements in the xenograft study, these measurements were captured by a scientist not involved in the project nor in the analysis to avoid bias in the collection of data.

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