Identifying tumor suppressor genes through an in vivo RNA interference screen, Pharm 551A: Bric et al., 2009

ResearchBlogging.orgOn the agenda for today is another paper about screening (one more to go after this one): Bric et al., (2009) Functional identification of tumor-suppressor genes through an in vivo RNA interference screen in a mouse lymphoma model [PMC]. This one is a different from our other screening papers because this one is not drug based. Rather, the authors have devised a screen to discover tumor suppressor genes in a mouse model of lymphoma. We’ll jump into all of this in a minute but first I want to take a minute to tell you why I chose this paper (which is way outside my area of expertise). Earlier this year I had the pleasure of attending the Rita Allen Foundation Scholars Meeting. This wonderful event was held in Princeton NJ and all of the current Rita Allen Foundation Scholars gave talks or presented posters. There I had the pleasure of learning about the work of Michael Hemann. Michael gave a really fantastic talk about work related to the current paper and I just couldn’t resist including some of this in my class. Having said that, this paper was more difficult to digest than I remembered from his talk and from my initial read of the paper when I was putting the syllabus together. It all seemed so much more simple when he was describing it in his talk, but, then again, that’s the sign of a great talk. We’ll see if I survive this today…

Back to the paper. As with all of the other screening papers we have done so far, the idea is simple: devise a screen for genes that suppress oncogenesis in vivo. To do this the authors have used a widely accepted lymphoma model and an RNA interference-based screen to find potentially novel tumor suppressor genes:

RNA interference facilitates loss-of-function genetics in mammalian cells and has been used to explore various aspects of cancer biology, including the function of tumor suppressor genes. Moreover, the availability of genome-wide libraries of shRNAs capable of stably repressing gene expression has enabled genetic screens for determinants of oncogenic transformation as well as potential therapeutic targets (Berns et al., 2007; Westbrook et al., 2005). To study cancer phenotypes not readily modeled in vitro, we have adapted RNAi technology to suppress tumor suppressor gene function in mice and have used this technology to study aspects of tumorigenesis, tumor maintenance, and treatment response (Hemann et al., 2003).

The Eμ-Myc lymphoma model expresses the c-myc oncogene in B cells (Adams et al., 1985) and has been used extensively for identifying lesions that promote tumorigenesis, either through retroviral-based insertional mutagenesis, by intercrossing with various transgenic or knockout mice or, more rapidly, by engrafting Eμ-Myc derived hematopoietic stem and progenitor cells (HSPCs) transduced with a gene or shRNA into syngeneic recipient mice (Schmitt and Lowe, 2002). Using the latter approach, we have shown that shRNAs targeting p53 or certain pro-apoptotic genes can mimic the corresponding gene deletion by promoting tumorigenesis (Hemann et al., 2003; Hemann et al., 2004). We therefore reasoned that it should be possible to introduce complex pools of shRNAs into Eμ-Myc progenitors, allowing for the selection of those capable of promoting tumorigenesis in transplanted recipients.

Onto the screen. Because a screen involving all possible genes would require a massive amount of work, the authors narrowed their screen down to 1000 genes that they call the “cancer 1000” set based on existing microarray data and literature mining. They then turned to the CODEX RNAi library to identify 2300 short hairpin RNAs (shRNAs) for effective knockdown of their genes of interest in cells isolated from their mouse model of lymphoma (Eu-myc). In preliminary experiments they figured out the extent to which they could dilute these shRNAs transfections and still get efficient oncogenesis when transplanting the cells into recipient mice. Based on this they divided their set of 2300 shRNAs into 48 pools of shRNAs with 48 shRNAs per pool (48×48 = just over 2300). They then transfected their cells from Eu-myc mice with pools of shRNAs and transplanted the cells into their recipient mice (3 mice per pool) and looked for development of lymphomas. They found that 27 of these pools caused lymphomas to develop in at least one mouse in the pool. To identify the shRNA they isolated genomic DNA from the lymphomas and amplified the shRNAs that were present. They reasoned that the shRNA that caused the lymphoma to develop would be enriched in the lymphoma in contrast to the overall pool of 48 shRNAs that were transfected into the cells from Eu-myc mice. This was exactly what they found and from the entire library they identified greater than 80 shRNAs that were enriched in tumors. Because these shRNA transfections would lead to knockdown of the gene of interest, the screen suggests that genes targeted by these shRNAs are involved in tumor suppression in lymphoma (at least in the context of myc overexpression).

Because 80 shRNAs was more than they could reasonably interrogate in the present study they decided to focus on several targets for validation:
1) Of 15 shRNAs that were highly enriched in tumors, 10 of these 15 showed accelerated tumor onset upon individual testing. It is not clear why 5 of these failed to cause tumor acceleration; however, it is formally possible that they required the additional presence of another shRNA in the pool to accelerate tumor progression.
2) Of 5 shRNAs that were present in tumors but not enriched, 2 of these 5 showed accelerated tumor onset when tested individually.
3) They also tested 6 shRNAs that were not found in tumors and upon validation none of these cause accelerated tumor onset.
All of this seems to validate the screen and suggests that a more in depth look at the 80 shRNAs initially identified, but not all validated, might have utility for further identification of tumor suppressor genes for lymphoma.

After all of this they go on to investigate 5 of the genes identified in the screen in more detail. I can’t write about all of this so I will just focus on MEK1 because I think this is the most interesting result in terms of the utility of the screen. MEK1 is a kinase and is an integral part of the mitogen activated protein kinase (MAPK) cascade. Specifically, MEK is an upstream kinase for ERK1/2. The interesting thing about MEK, from the perspective of their screen, is that MEK1 can have pro-oncogenic properties in other contexts and but this specific screen was for tumor suppressors:

In validating our findings, we were surprised that some of the genes we identified as tumor suppressors have pro-oncogenic properties in other contexts. Thus, while angiopoietin 2 was identified as an anti-angiogenic protein (Maisonpierre et al., 1997), it can also be pro-angiogenic in vivo (Lobov et al., 2002). Likewise, Mek1 can transmit oncogenic signals downstream of ras (de Vries-Smits et al., 1992), but is also required for the transmission of checkpoint signals in response to both oncogenic and genotoxic stress (Lin et al., 1998; Zhu et al., 1998; Yan et al., 2007). As expected, tumors triggered by Mek1 shRNAs displayed reduced Mek1 expression, corresponding to a lower level of phospho-ERK1/2, two downstream targets (Figure 4A). Interestingly, acute activation of Myc triggered the phosphorylation of the Mek targets Erk1/2 in a Mek-dependent manner (Figure 4B), and treatment of cells with a Mek1 inhibitor attenuated Myc-induced cleavage of the apoptosis effector PARP, as well as activation of p53 and the DNA damage response proteins RAD17 and γH2AX (see below) (Figure 4C,D). Furthermore, primary B-cells co-expressing Myc and a Mek1 shRNA were selected for in an in vitro competition assay, whereas cells expressing the Mek1 shRNA alone depleted over time (Figure 4E-F). Together, these data imply that Mek1 is a context dependent tumor suppressor whose anti-proliferative action is revealed in Myc-expressing cells.

If I remember correctly (not sure if I am remembering anything correctly anymore from the amount of travel and meetings I’ve been doing lately), this was a major point of Michael Hemann’s talk at the Rita Allen meeting. It is somewhat obvious that context matters but the issue is how to figure this out relatively quickly. In my view, this sort of screen has real potential utility for moving ahead in this area.

Bric, A., Miething, C., Bialucha, C., Scuoppo, C., Zender, L., Krasnitz, A., Xuan, Z., Zuber, J., Wigler, M., & Hicks, J. (2009). Functional Identification of Tumor-Suppressor Genes through an In Vivo RNA Interference Screen in a Mouse Lymphoma Model Cancer Cell, 16 (4), 324-335 DOI: 10.1016/j.ccr.2009.08.015

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