Injecting molecules from a sea slug that received tail shocks into one that didn’t made the recipient animal behave more cautiously.
Two freely available databases include data on hundreds of human cancer cell lines.
July 27, 2017|
NOVARTISIn what appear to be the largest RNAi screening efforts in cancer to date, two groups of scientists have tamped down the expression of thousands of genes in hundreds of human cancer cell lines. Their results, published today (July 27) in two Cell papers and made freely available to researchers, confirm the roles of the usual genetic suspects in cancer and identify new potential therapeutic targets.
The Scientist spoke with the lead author of one of the studies, Rob McDonald, a senior investigator at Novartis Institutes for BioMedical Research, about his team’s Project DRIVE endeavor. The study systematically knocked down more than 7,800 genes in nearly 400 cell lines. The other project, by researchers from the Broad Institute and Dana Farber Cancer Institute, looked for genetic dependences for cancer growth or survival among 501 cell lines.
The Scientist: What is the goal of Project DRIVE?
Rob McDonald: Very simply, it was to identify new therapeutic targets across a variety of cancer types that would hopefully impact patient care.
TS: How did you go about it?
RM: It really started with a foundational tool we have here at Novartis called the cancer cell line encyclopedia. It was developed a number of years ago in collaboration with the Broad where we collected over 1,000 different cell line models and then proceeded to characterize them for mutations and gene expression and the like.
It was our attempt to functionally annotate the cancer genome. The original phase characterized mutations and expression, but it really didn’t tell us what genes each cancer type cared about. That’s what Project DRIVE aimed to identify. So within a particular cancer type, let’s say melanoma, what genes does melanoma care about, and are they the same or different than what lung cancer cares about?
TS: How did you select the genes included in this screen?
RM: Best case scenario, we would have screened the genome, but one thing that was really important to us was to actually have that depth as far as shRNAs [short hairpin RNAs] per gene. We have 20 shRNAs per gene. That really gave us confidence in the dataset. So because we had so many reagents per gene, we did have to be a little selective about the genes we included.
We were sure to include things we know would score, [such as] oncogenes that are mutated. . . . We also looked at what genes were expressed in the cancer cell line encyclopedia.
Ultimately, we are interested in making drugs. [W]hether or not we thought the protein would be druggable was also a factor.
TS: What were some of the highlights of what you learned from this?
RM: The predictable ones are the ones where the target of the shRNA is, let’s say, mutated, like an oncogene like KRAS or BRAF. The synthetic lethal cases tend to be a more complex relationship. [In synthetic lethality, the deficiency of a set of genes causes trouble, but knocking down any one of those genes alone has no effect.] You’ll see a cancer dependence in a subset of cell lines, but the gene you’re knocking down is not directly mutated or highly expressed.
Then you need to figure out what that relationship is between the genetic dependence and the cell lines that are sensitive. That’s where the fun begins as far as I’m concerned.
TS: How can researchers access this dataset?
RM: There’s a web portal that will allow anyone to go in and look up their favorite gene and see what that phenotype is across 400 cancer cell lines. That was really important to us to further the cancer research community.
The other facet of that web-based tool is you can go in and also enter a gene of interest but then ask the question, what genes that were tested had a similar phenotype? Or, which other genes behaved similarly across the dataset? In that way you can start to understand protein complexes and protein networks.
The interview was edited for brevity.
E.R. McDonald et al., “Project DRIVE: A compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening,” Cell, 170:577-92, 2017.
July 28, 2017
Cancer genes fall into two main categories: cancer-causing genes, CCG, that drive malignant transformation and maintain tumor growth, and CAN genes that orchestrate local invasion and further spread of metastatic cells. CCG show high mutation rates (~100%) while the CAN genes show low mutation rates (5-10%). The number of genes involved in causation and cancer spread may be estimated from death frequencies as function of age (6-8 genetic steps to death from cancer to be in the range of 3-4 assuming both alleles affected; allowing haploinsufficiency for some of these genes the number may be higher but not to exceed 4-6. This small number is therefore responsible for malignant transformation, initial local growth and finally cancer spread by invasion and metastasis culminating in the death of the patients. The obvious discrepancy between this estimate and the much larger number of cancer genes as reflected in various “gene signatures” (http://cgap.nci.nih.gov/) suggests that most cancer genes associated with a particular cancer are not mutated. The over-/under-expression of these genes results from altered function of the original small set of mutated genes and their downstream targets; among these earliest targets there might be genes (“relay genes”) that multiply/diversify the genetic pathways (like HIF 1 and 2 alpha and BHLHB2). The total number of cancer genes assuming there are about 200 different human cancers will not much exceed 6,000-12,000 assuming 10%-5% mutation rates for CAN genes respectively. The number of CCG may not exceed 200. It is important to emphasize the fundamental role of VHL in tumor progression and creation of cancer stem cells (CSC).