Cell panel screens play nicely with CRISPR screens

Getting a long list of potential genes that influence the response to your new therapeutic is one sign of a successful CRISPR screen, but the hard work has only just begun. How do you prioritize this list for targets that could have an impact in the clinic? Bioinformatics and data mining can help, but other screens can also add value, including large cell panel screens with the same therapeutic of interest.

Pooled Screening workflow
Pooled Screening workflow

A flexible CRISPRko library to fit any workflow

Horizon scientists have recently built a new, whole genome CRISPR knockout library that enables the use of 4, 6 or 8 guides, depending on the biological question being addressed and the modality being used for the screen. For example, eight guides could be used to find genes that when knocked out result in sensitivity to a new drug in cancer cell lines in culture (a drop out screen), whereas a four guide library might be best placed for in vivo CDX based CRISPR screens where there is a need to keep the library small.

A proof-of-principle screen

To test the utility of this new CRISPRko library, a whole genome drug–gene interaction screen was carried out in the HT 29 cancer cell line using the PARP inhibitor olaparib. The screen successfully identified genes that when knocked out either increased or decreased sensitivity to the drug compared with DMSO treated control cells.

So far, so good, but how best to triage this list?

Large cell panel screens, using many cancer cell lines representing different cancer types, can also be informative in identifying sensitivity and resistance mechanisms. Horizon scientists tested olaparib and an additional PARP inhibitor (talazoparib) across a panel of more than 300 cancer cell lines. Using these data and drug response and CRISPR screen data from the DepMap portal, a set of genes influencing sensitivity and resistance to these drugs was also compiled.

Substantial "hit" overlap with complementary methods

The genes influencing sensitivity and resistance to olaparib and talazoparib in the cell panel screen had substantial overlap with the genes identified in the CRISPR screen. Genes involved in DNA damage response, cell survival and cell division pathways featured heavily. A combination drug screen using clinical grade compounds was used to initially validate some of the genes whose loss seems to increase sensitivity to olaparib. Drug-mediated inhibition of proteins involved in DNA damage, such as ATM, ATR, CHK1 and CHK2; proteins involved in cell survival, such as BCL-XL; and proteins involved in cell division, such as WEE1, all increased sensitivity to olaparib.

Our integrated pooled CRISPRko and high throughput cell panel screening platform offers more clinically relevant information

Assessing overall concordance between our CRISPRko screen in HT29 cells and similar CRISPRko screens published in other cell types using olaparib shows that agreement between CRISPR screens carried out in different labs is substantially greater than that observed in RNA interference screens. However, differences between cell lines are still evident, indicating that robust conclusions from drug-gene interaction screens are best made from screens conducted across diverse genetic and lineage backgrounds, to better describe the drug–gene effects. This can be achieved with our fully integrated pooled CRISPRko and high throughput cell panel screening platform. The orthogonal approach of cell panel screening delivers rapid initial validation of selective hits in high throughput and enables targets to be considered in terms of their tractability in the clinic. These data also illustrate how CRISPR screens and high throughput cell panel screens can be used to rapidly validate new, clinically relevant genetic interactions.

Figure 5 AB Blanck 2020
AB Blanck 2020
Figure 5: Validation of CRISPR screening hits using combinations. (A) Heat-map representation of synergy scores from PARPi combinations across a panel of 10 cell lines. Cell viability was assessed following a 6-day treatmentusing CellTiter-Glo 2.0. A baseline T0 measurement (at time of drug addition) was taken to enable calculation of GI.The synergy scores were calculated from each dose matrix using the Loewe additivity model. (B) Representativedose matrices for combinations of PARP inhibitors with ATM, BCL-XL/2, and BET antagonists. For each combination,GI values (left matrix) and the Loewe excess values (right matrix) are shown. Loewe excess values are determinedby subtracting the level of inhibition predicted as additive by the Loewe model from the actual observed inhibition.Therefore, positive excess values indicate synergy and negative values indicate antagonism. The synergy score foreach matrix is shown below each example in gold text. A minimum synergy score of 2.1 was considered thethreshold for the combination to be considered synergistic and values over 12 are considered strong synergies. An example of both a strong and a more moderate synergistic interaction are shown for each combination.

Nicola McCarthy, Global Translational Science Liaison, Horizon DiscoveryWritten by Dr. Nicola McCarthy

Dr, McCarthy has 10 years of postdoctoral research experience studying apoptosis (programmed cell death) in the context of cancer research and cardiovascular research and studied human anatomy for her B.Sc. and programmed cell death for her Ph.D. at the University of Birmingham, UK.

Read the full publication in The CRISPR Journal

A flexible, pooled CRISPR library for drug development screens. Blanck, M., et al. CRISPR J. 3: 211-222 2020. doi: 10.1089/crispr.2019.0066

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