Translating from Scientist to English
Stories were coming out recently about new cancer research led by Bryony Telford in Parry Guilford’s lab at Otago, and I’d thought I’d use it for an example of translation from Scientist to English. It’s a good example for news because it really is pretty impressive, because it involved a New Zealand family with familial cancer, and because the abstract of the research paper is well written — it’s just not written in ordinary English. Combining the abstract with the press release and a bit of Google makes a translation possible.
This will be long.
The CDH1 gene, which encodes the cell-to-cell adhesion protein E-cadherin, is frequently mutated in lobular breast cancer (LBC) and diffuse gastric cancer (DGC).
Background: the name of the gene and protein, and why we care. I can guess that these aren’t extremely common cancers, because (a) I would know about them, and (b) it would say so. LBC turns out to be about 10% of breast cancer.
However, because E-cadherin is a tumor suppressor protein and lost from the cancer cell, it is not a conventional drug target.
A tumour suppressor protein prevents cancer when it works, and fails to prevent cancer when it is missing. In contrast to proteins that increase cell growth when present, where you can try to make a drug to stop the protein working (like Herceptin), a tumour suppressor protein isn’t there in the cancer cell. There’s nothing to stop.
To overcome this, we have taken a synthetic lethal approach to determine whether the loss of E-cadherin creates druggable vulnerabilities.
Hopefully, the tumour suppressor protein does something important for cells, so losing it means they have to rely on some sort of relatively fragile work-around. We can try to break that work-around.
Synthetic lethality is a genetics term, where a mutation in either of two genes is survivable, but the cell dies when both genes are disabled. The researchers were looking for proteins that normal cells could live without, but that were essential for cancer cells missing E-cadherin.
We first conducted a genome wide siRNA screen of isogenic MCF10A cells with and without CDH1expression.
They compared cells that were the same (isogenic) except for the E-cadherin gene. They used <technology> to do this (ok, you could look up siRNA,but Wikipedia’s not that readable, and the simplest reference I could find was an ad)
Gene ontology analysis demonstrated that G-protein coupled receptor (GPCR) signalling proteins were highly enriched amongst the synthetic lethal candidates.
Using <technology> they found lots of the potentially-useful proteins were of one type, called GPCRs. This is important because there are lots and lots of drugs that target GPCRs.
Diverse families of cytoskeletal proteins were also frequently represented.
Quite a lot of the potentially-useful proteins were of other types.
These broad classes of E-cadherin synthetic lethal hits were validated using both lentiviral-mediated shRNA knockdown and specific antagonists including the JAK inhibitor LY2784544, Pertussis toxin and the aurora kinase inhibitors Alisertib and Danusertib.
You can’t necessarily rely on results from one <technology>, so they used different <technology> to check the synthetic lethal proteins were really real. This is important — the high-volume approaches to biological testing can go wrong, and you don’t want to waste time, money, and PhD students on false hits.
Next, we conducted a 4,057 known drug screen and time course studies on the CDH1 isogenic MCF10A cell lines…
There are lots of drugs already available that might target the proteins they found, which is good because we know these are safe (at least by the relatively low standards of cancer treatment). They tried lots of these drugs on their cancer cells to see if they would kill them.
…and identified additional drug classes with linkages to GPCR signalling and cytoskeletal function that showed evidence of E-cadherin synthetic lethality.These included multiple histone deacetylase inhibitors including Vorinostat and Entinostat, phosphoinositide 3-kinase inhibitors, and the tyrosine kinase inhibitors Crizotinib and Saracatinib. Together, these results demonstrate that E-cadherin loss creates druggable vulnerabilities that have the potential to improve the management of both sporadic and familial LBC and DGC.
The cancer cells died. The science was successful. These drugs might be helpful in real people, too.
When you’re translating from Scientist to English it can be useful just to use <technology> or <gene name> or even <thing> for something you don’t understand, to see if the translation still works. I actually know roughly what siRNA is, but I didn’t know what shRNA was, and all I know about LY2784544 is that it’s probably the internal drug-company name for some chemical — either a drug candidate or a research tool. At the level I care about, let alone the level a journalist is likely to care about, none of these matter.
If you compare this to a lot of stories about how some chemical in coffee beans or grape seeds might cure cancer, there are three big differences. The first is the scale: they looked at every gene in the cell, and ended up doing more than 4000 tests of drugs.The second is the specificity: this is a study of just two types of cancer with the same genetic change. The third is the care taken (more obvious in the full paper) not to find spurious associations.
It’s still important not to oversell the medical importance: these might not work in real tumours (size matters), or the cancer cells might be able to mutate again and bypass the treatment. Even if they work, they will be helpful for a small subset of cancer patients. Here’s a good discussion of drugs using a similar approach to BRCA mutations.
Still, this is an example of research using powerful modern tools, targeting problems that matter to people in New Zealand. It’s the sort of lab-based cancer research that makes good news stories, and should.
Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »