A Screen of shRNAs Targeting Tumor Suppressor Genes to Identify Factors Involved in Paclitaxel Sensitivity

Diana Ji, Stacy L. Deeds, and Edward J. Weinstein

RNA interference methodologies have been utilized in small to large scale screening projects. The technology has allowed researchers to perform gene-silencing experiments in timeframes and target cells previously not possible. While siRNA screens have become fairly common, large scale screening with shRNA is still evolving. Advantages of shRNAbased experiments include long-term knockdown and viral delivery to non-transfectable cell types.

We set out to develop strategies for using lentiviral-based shRNA libraries in larger scale silencing projects (see Figure 1 for library vector map). Pilot screens using a tumor suppressor gene family set were performed to address biologically relevant questions while simultaneously developing screening strategies that could be used by a variety of researchers in the field.

We set out to develop strategies for using lentiviral-based shRNA libraries in larger scale silencing projects (see Figure 1 for library vector map). Pilot screens using a tumor suppressor gene family set were performed to address biologically relevant questions while simultaneously developing screening strategies that could be used by a variety of researchers in the field.

One potential treatment for lung carcinoma is paclitaxel. This anti-cancer agent (produced by Bristol-Meyers Oncology) functions by stabilization of microtubules so they cannot depolymerize. It effectively shifts the equilibrium in cells towards microtubule assembly, disrupting the normal operation of the microtubule network and thereby arresting the mitotic process.

Treatment with paclitaxel alone, however, is effective in only a fraction of the population. Approximately 21% to 24% of patients with non-small cell lung carcinoma (NSCLC) will respond to a regimen with this single agent. As a result, first line therapy may involve multiple chemotherapeutics, often combining paclitaxel with one, or sometimes two, additional chemotherapies, including cisplatin, carboplatin, or radiation treatment.

Since chemotherapy is toxic and stressful to a patient, there is an obvious benefit to limiting the amounts of these toxic compounds administered. If patients who are likely to positively respond to paclitaxel as a single agent could be identified prior to treatment, they may be spared the unnecessary pain associated with combination regimens. One potential way to do this is through employment of pharmacogenomics.

The first step in a potential strategy would be to categorize patients based on the molecular profile of their tumor. This can be effectively accomplished through use of microarrays (on an RNA level), array CGH (on a DNA level), or protein chips. This type of work is well established and has been thoroughly reviewed elsewhere.

After understanding the profile of a tumor, one still must determine which molecular signature is indicative of responsiveness to a drug, and which is not. We utilized an shRNA screen to help elucidate this question, specifically in the case of NSCLC and paclitaxel treatment.

Various transcripts were down-regulated using lentiviral-based shRNAs found in a panel targeting tumor suppressor genes (Sigma-Aldrich, MISSION^™ TRC shRNA Human Tumor Suppressors, Cat. No. SH0531) in lung cancer cells grown under standard conditions. Transductions were performed in 96-well plates, with each well receiving cells and a single shRNA delivered by lentiviral particles. The entire tumor suppressor panel, consisting of approximately 75 gene targets each represented by 3-5 individual shRNA clones, fits onto five 96-well plates. After selection of transduced cells with puromycin, each well was split1:2 (2 sets of 5 x 96-well plates). One set of plates was “mock-treated” while the second set was treated with paclitaxel. Cell growth was then assessed in all wells (Figure 2). All values were normalized to a negative control (cells infected with an empty vector-containing lentivirus).

This screen allows one to identify genes involved in cell survival, and more importantly, it is designed to identify which shRNAs (and therefore which genes) will assist in making a cell more sensitive, or more resistant, to paclitaxel (Figure 2). We identifi ed several genes that lead to increased resistance to treatment. This correlates with published examples of cases in which patients carrying mutations or deletions of tumor suppressor genes have a lower rate of response to a variety of therapies. Prominent among those genes is p53, which was found to play a role in paclitaxel resistance in our screen as well.

More intriguing is the finding that some genes, when down-regulated by these shRNAs, lead to increased sensitivity of cells to paclitaxel (Figure 3). Again, there is evidence in the literature to support this finding. BRCA1 has been found to be mutated in breast and ovarian tumors, and patients with these mutations can be more responsive to chemotherapies than patients with this gene intact. Patients with renal cell carcinoma and a mutation or truncation in the VHL tumor suppressor gene have better response rates and a longer time to tumor progression when treated with a certain molecular therapy than patients with a functioning VHL. The gene VHL was also identified in our screen.

It is important to note that these experiments are preliminary and it is difficult to conclude the role of specific genes identified and their potential relevance to actual clinical outcomes, however, the implications are promising. Our experiments imply that by examining the molecular state of a tumor and determining the levels of certain tumor suppressor genes, one might be able to determine the likelihood of response to a therapy. Patients with tumors containing high levels of some genes, such as p53, would be expected to respond less well to paclitaxel, perhaps falling into the group of 80% “non-responders,” while patients with low levels of other genes, such as VHL, would be expected to respond better to this drug (perhaps falling into the 20% clinical “responder” population).

An advantage to the use of shRNA is that it allows an investigator to rapidly validate results in multiple model systems. We are encouraged by our findings in this one nonsmall cell lung carcinoma line and want to attempt to validate the results in additional lines. We are similarly interested in determining whether these genes play a role only in NSCLC, or if they also affect response rates in cell lines derived from other tumor types. We can do this fairly simply with lentiviral delivery of shRNA with little, or no, additional optimization of conditions necessary. Unlike the more traditional siRNA approach, we do not need to optimize transfection or reaction conditions for each new cell line – transduction is usually as simple as growing cells and adding lentivirus to the media. Also unlike siRNA, shRNA allows one to have a longterm down-regulation of a gene. This will facilitate our moving from an in vitro setting to an in vivo experiment. After confirmation of our results in mouse xenograft models, we hope to examine the clinical setting to see if patients who have been treated with paclitaxel in the past have a gene expression profile correlating with our findings. If so, these elucidated genes will have exciting possibilities, either as targets or biomarkers for paclitaxel sensitivity.

For more information on the MISSION TRC shRNA collections, visit sigma-aldrich.com/rnai

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