Here we identified tumor suppressor genes targeting an array of biological processes by conducting a forward genetic screen using a biologically relevant endpoint – tumorigenesis. Although further studies will explore how each gene acts to suppress tumorigenesis, several have biological activities not readily assayed in vitro. Notably, our screen was not exhaustive: improvements in shRNA knockdown efficiency, a broader screen, a larger cohort of animals, and/or expansion to other tumor models will undoubtedly yield additional relevant genes. Hence, this study, when placed in the context of other studies to functionally identify cancer genes, implies that there are a surprisingly large number of genes that, when deregulated in an appropriate genetic background, can contribute to malignancy.
Our approach conceptually parallels the replication competent retrovirus-based insertional mutagenesis screens that have identified candidate oncogenes in the Eμ-Myc model and other systems (Uren et al., 2005
). However, none of our top 15 candidate tumor suppressors were identified as sites of common insertions in Eμ-Myc or other lymphoid-based insertional mutagenesis screens (Akagi et al., 2004
), suggesting that shRNA screening interrogates a distinct set of genes. Our shRNA-based approach allows a defined selection of genes to be screened and, owing to the trans-acting effects of RNAi, one integration is in principle sufficient to inactivate gene expression from two alleles. Thus, our approach complements insertional mutagenesis screens and identifies yet additional uses for the well-characterized Eμ-Myc mouse model.
In parallel to the current study, we also conducted an RNAi screen using a mouse model of hepatocellular carcinoma (Zender et al.; Cell 2008
). In this setting we chose shRNAs targeting the mouse orthologs of genes deleted in human HCC as a guide to enrich the RNAi library for tumor suppressor genes. By expanding to a different in vivo model in this study and employing a more broadly defined set of shRNAs, we discovered tumor suppressor genes that would not have been identified based on genomics data alone. Interestingly, preliminary screening of the Cancer1000 library in the HCC model uncovered candidate tumor suppressors not identified in the lymphoma screen, while several hits from the lymphoma screen did not accelerate liver tumorigenesis (L.Z., W. Xue, S.W.L., unpublished observations). These observations indicate that many tumor suppressor genes function in a context dependent manner and highlight the value of conducting shRNA-based screens in multiple tumor models.
We chose to investigate in detail one of our candidate tumor suppressor genes, rad17
. Together, our data support a model where Rad17 acts as a haploinsufficient tumor suppressor by mediating replication stress from oncogenes to both p53-dependent and independent anti-proliferative responses. Alleviation of this effect allows proliferation to continue inappropriately. Of note, lymphomas triggered by the most oncogenic Rad17.1169 shRNA retained a wild type p53
gene and intact p53 response, suggesting that p53
-loss is not required for Rad17 suppression to promote tumorigenesis (data not shown). Therefore, while attenuation of Rad17 activity may eventually lead to genomic instability and contribute indirectly to tumorigenesis, we believe the more direct effect on the cell cycle described here is likely to explain its action as a tumor suppressor in our system. In line with previous studies, we found Chk1 activation to be Rad17 dependent. Interestingly, Chk1 also displays phenotypes consistent with a haploinsufficient tumor suppressor, namely deregulated cell cycle entry, accelerated tumor development and, if homozygously deleted, embryonic lethality due to excessive DNA damage (Liu et al., 2000
; Lam et al., 2004
The identification of Rad17 as a tumor suppressor demonstrates the potential of shRNA-based screens to discover and validate haploinsufficient tumor suppressors whose partial loss of expression is pro-oncogenic, but whose complete loss of function is deleterious for pre-neoplastic cells (Payne and Kemp, 2005
). Based on the variable potencies of different shRNAs that target the same gene, in vivo
RNAi screens are able to survey a broad dynamic range of target gene expression for which those cells with optimal knockdown will be selected for during tumorigenesis. In support of this concept, Rad17 shRNAs that induce distinct levels of knockdown following acute introduction into cell populations feature a more homogeneous suppression of Rad17 in the outgrown tumors (compare and ).
Importantly, genomic deletions found in human tumor samples are often hemizygous, and it is often assumed that relevant tumor suppressors must display concomitant loss or suppression of the remaining wild type allele. Indeed, reduced expression of Rad17 is observed in human diffuse large B-cell lymphoma (DLBCL) and this correlates with poor prognosis. While it remains to be determined whether hemizygous deletions involving Rad17 occur in DLBCL, they occur and are common in human colon and breast cancer. As such hemizygous deletions in cancer cells can be quite large, there may be many other genes that can contribute to cancer when reduced to a single copy. Since, in these instances there is no clear mutation in a second allele, it is difficult to determine their relevance though genomic approaches alone.
We were surprised that some genes with putative oncogenic properties were identified in our screen, implying that many genes can act both pro- or anti-oncogenic depending on genetic or cellular context. As an example, Mek1, a critical effector in the MAPK pathway, scored in all our assays. While seemingly paradoxical, these studies are consistent with previous work showing that Mek is required for DNA checkpoint activation in response to genotoxic stress (Yan et al., 2007
). Antiproliferative functions of Mek have furthermore been corroborated by studies demonstrating that high dose MAPK signaling can produce antiproliferative responses (Olson et al., 1998
), and studies suggesting that, in premalignant cells, Mek is required
for Ras-induced senescence – a tumor suppressive program (Lin et al., 1998
; Zhu et al., 1998
). In addition, Mek1 inhibition may destabilize Myc (Sears et al., 2000
), enabling proliferation without apoptosis (Murphy et al., 2008
), or interfere with a feedback mechanism that would otherwise dampen proliferation (Pratilas et al., 2009
). Whatever the precise mechanism whereby Mek1 suppression accelerates tumorigenesis, our data, together with published reports, emphasize that the physiological response to Mek1 inhibition is highly context dependent and strongly influenced by the genetic background in which it occurs.
Although our goal was to identify genes that limit tumorigenesis, our results have therapeutic implications. First, since many chemotherapeutic agents trigger a DNA damage response whose integrity can influence treatment outcome, knowledge of RAD17
status in tumors may help guide the use of chemotherapy in patients. Second, owing to their pro-oncogenic activities in certain settings, some of the tumor suppressors we identified (e.g. MEK1 and ANG2) are targets of inhibitors in clinical trials (Rinehart et al., 2004
) – our observations hint that contextual information may be required for the effective use of these inhibitors in the clinic. Finally, our screen identified several tumor suppressor genes that encode secreted proteins, including Sfrp1
, and Bmp3
(Supplementary Table 2
, see also Zender et al., 2008
). As shRNAs targeting these genes were isolated from pools of cells in which only a portion contain a particular shRNA, it is likely that these factors operate either in an autocrine manner, or as short range paracrine signals that alter the microenvironment in ways that stimulate tumorigenesis. Still, if loss of these proteins is required to sustain tumor progression, systemic delivery of recombinant proteins or peptides may have therapeutic utility [(see, for example, (Wajapeyee et al., 2008
)]. It seems likely that these and other high-throughput methods to functionally identify cancer genes will produce further insights into the complexities of cancer development and point towards new therapeutic targets.