Therapeutic approaches using multiple drug combinations have become a standard treatment model for many types of cancer. Due to the tremendous genetic complexity and adaptive nature of most human malignancies, the use of multiple drugs acting on different targets increases the efficacy and helps thwart the development of drug resistance. However, the search for new treatment options and expanding number of drug candidates create a demand for better understanding and prediction of the most effective combinations to expedite evaluation and application in clinical settings.
To help address this challenge, Cellecta responded to an NIH contract request to develop new tools that help assess the effect of combinatorially silencing pairs of genes. We developed a variation of our RNAi pooled screening that systematically identifies and prioritizes gene pairs that, when knocked down, significantly inhibit cancer cell growth. In other words, rather than simply identifying which individual genes are essential for growth of cancer cell lines, we identify which pairs that, when silenced, most significantly inhibit cancer cell proliferation.
In some cases, the loss of two genes may be additive and strongly impair cell growth much more significantly than the loss of either gene independently. In fact, sometimes either gene independently may not have any negative effect on cells but, when both are knocked down, there is a synergistic effect that is very lethal to cells. Conversely, losing the function of two known essential genes may not, in fact, have any more of an adverse affect on cell proliferation than the loss either separately. As a result, then, it is very difficult to predict the effect of a loss of a pair of genes so each combination must be tested.
For this project, we made a specialized lentiviral vector containing two shRNA expression cassettes so the construct expresses two different shRNAs from independent promoters. A library of shRNAs was cloned into each of these shRNA expression cassettes to make a pooled heterogeneous population that expressed all paired combinations of shRNAs. With some cloning tricks, we were able to incorporate a short uniquely identifiable sequence (i.e., a “bar-code”) that identified which two shRNAs were in each vector.
The data below were generated with four shRNAs designed against each of 40 DNA damage and repair genes (160 shRNAs total) so, on completion, there were 25,600 different combinations—160 in the first shRNA position vs. 160 in the second. Using this library, we ran an RNAi lethality screen with an isogenic panel of immortalized human mammary epithelial (HMEC) cells using our standard procedures. We have validated several of the pairs and confirmed the combinatorial effect on cell growth. The approach can be reasonably extended to systematically test all combinations of approximately 200 targets in a single screen.
Obviously, this approach provides an alternative to what would otherwise be extremely time consuming and expensive pair-wise individual assays to assess lethal gene knockdown combinations in large numbers of target genes. Moreover, it demonstrates the power and flexibility of pooled library screens to address the challenges of elucidating the multiple functional roles and importance of various genes in the variety of biological model systems used in life science and drug discovery research.