Skip to main content
Premium Trial:

Request an Annual Quote

New Targets


Members of the MYC oncogene family have long been considered undruggable because they encode transcription factors and carry out essential functions in proliferative tissues. Using a high-throughput siRNA screening approach, the Fred Hutchinson Cancer Research Center's Carla Grandori and her colleagues identified a network of genes that showed synthetic lethal interactions with oncogenic overexpression of MYC. Then, using RNAi and an inhibitor of the kinase CSNK1e, the researchers showed in a mouse model that the drug slowed and eventually halted the growth of MYCN-amplified neuroblastoma xenografts, as they reported in PNAS in May. Genome Technology's Tracy Vence recently spoke with Grandori to find out more. What follows is an excerpt of that conversation, edited for space.

Genome Technology: Why is MYC considered 'undruggable'?

Carla Grandori: MYC is a DNA-binding protein so it doesn't have enzymatic function or druggable domains. However, it needs a partner in cells to bind DNA and perform functions. People have tried to use small molecules to block the interaction with its partner, but the problem is the surface of interaction is really huge and small molecules really are not suited for that type of inhibition. The second reason is MYC is essential for all cells to proliferate. So, how we've done it is to try to block cells that are under [oncogenic] over-expression [of MYC], but not cells under normal levels of MYC.

GT: Why did your group choose an siRNA-based approach?

CG: One could go and select candidates in part of the pathway and guess that if you target these, you could maybe alter some function of MYC that are only essential in proliferative cells — that's kind of the old--fashioned, hypothesis-driven approach.

Here, with the siRNA approach — these [siRNAs] are chemically synthesized, you put the exact same amount in each well and you add your cancer cells or vice versa ... and then you can measure the effect of each individual siRNA. Basically, if I knew nothing about a compound, the screen would tell me all the functional targets of the pathway related to it, precisely, as if it was a textbook.

GT: How did you decide which candidates to follow up on?

CG: Normally, when you do a screen the old-fashioned way, you just take your best candidate. Instead, because these screens are very precise, for every gene there is a strength [estimate]. So, inhibiting Gene X gives you 50 percent inhibition of growth, inhibiting Gene Y, you get maybe 30 percent, and so forth. I don't look at it that way. I take all the genes from our screen and build a network, just like it was a social network: Who is talking to MYC? Some of the genes might not be as strong [of an] inhibitor, but I see they are very -connected to MYC, and perhaps they are highly druggable. So, these are the genes to go after. I went after one gene for proof of principle.

GT: What made you select CSNK1e?

CG: It had an inhibitor that was not designed for oncology.

GT: How did you get your hands on this inhibitor?

CG: Google!

GT: In your preclinical study on 10 mice, the treatment group of five survived longer and showed reduced tumor volume. Were you surprised?

CG: I didn't expect it to work so well. [However,] I don't want to give the impression that we have a wonder drug in hand. It clearly hits this kinase, but the real proof of specificity comes when we used the shRNA in vivo and the tumor didn't grow.

GT: What's the take-home message?

CG: That we can hope to target cancer cells not just by targeting what's mutated or very abnormal. By targeting normal cellular genes that are acting as supporters of genetic mutants, that broadens the druggable targets for cancer. Functional genomics will pinpoint new targets for therapy that DNA sequence cannot tell us.

The Scan

UCLA Team Reports Cost-Effective Liquid Biopsy Approach for Cancer Detection

The researchers report in Nature Communications that their liquid biopsy approach has high specificity in detecting all- and early-stage cancers.

Machine Learning Improves Diagnostic Accuracy of Breast Cancer MRI, Study Shows

Combining machine learning with radiologists' interpretations further increased the diagnostic accuracy of MRIs for breast cancer, a Science Translational Medicine paper finds.

Genome Damage in Neurons Triggers Alzheimer's-Linked Inflammation

Neurons harboring increased DNA double-strand breaks activate microglia to lead to neuroinflammation like that seen in Alzheimer's disease, a new Science Advances study finds.

Long COVID-19 Susceptibility Clues Contained in Blood Plasma Proteome

A longitudinal study in eBioMedicine found weeks-long blood plasma proteome shifts after SARS-CoV-2 infection, along with proteomic signatures that appeared to coincide with long Covid risk.