Academic-industry collaboration yields comprehensive encyclopaedia of genetic and molecular information and enables predictive modelling of anti-cancer drug sensitivity
An academic-industry collaboration, described in the March 29 online issue of the journal Nature, releases the first results from a new and freely available resource that marries deeply detailed cancer genome data with predictors of drug response - information that could lead to refinements in cancer clinical trials and future treatments.
The Cancer Cell Line Encyclopaedia is authored by scientists at the Broad Institute, Dana-Farber Cancer Institute, the Genomics Institute of the Novartis Foundation, and the Novartis Institutes for Biomedical Research. In a proof of principle, the researchers also report that genomic predictors of drug sensitivity revealed three novel candidate biomarkers of response.
According to Levi A. Garraway, a senior associate member of the Broad Institute, an associate professor at Dana-Farber Cancer Institute and Harvard Medical School, and a co-corresponding author of the paper, the Cancer Cell Line Encyclopaedia has the potential to serve as a preclinical resource that could guide clinical trials. According to Todd Golub, director of the Broad’s Cancer Program, Charles A. Dana, Investigator in Human Cancer Genetics at the Dana-Farber Cancer Institute, and a co-author of the paper, the challenge now is to greatly expand the number of compounds tested across the panel of cell lines.
Great depth of genetic characterisation and pharmacological annotation
The Encyclopaedia integrates gene expression, chromosomal copy number, and massively parallel sequencing data from almost 1,000 human cancer cell lines together with pharmacological profiles for 24 anti-cancer drugs across roughly half of these cell lines. The scale of the project allows greater depth of genetic characterisation and pharmacological annotation than previously possible with fewer cell lines.
To accomplish such a feat, the team of scientists relied on the genetics, computational biology, and drug-screening capabilities at the Broad, Dana-Farber, and Novartis. They chose 947 of the nearly 1,200 commercially available cancer cell lines to reflect the genomic diversity of human cancers.
The cell lines were acquired from commercial vendors in the USA, Europe, Japan and Korea and represent a diverse picture of cancer as a disease as they include many subtypes of both common and rare forms of cancer. According to Nicolas Stransky, a computational biologist in the Cancer Program at the Broad and a co-first author of the paper, one of the strengths of this Encyclopaedia lies in the number of cell lines it surveys, so researchers can focus on rare cancer subtypes and still will have sufficient statistical power for analyses.
Cancer cell lines are malignant cells that have been removed from tumour tissue and cultured in the laboratory. Under controlled conditions, they can grow indefinitely. This near-immortality is an advantage for performing repeated experiments, but it can be a potential pitfall if the cells differ markedly from tumours because they lack typical surroundings. However, with relatively few exceptions, the Encyclopaedia cell lines proved to be representative genetic proxies for primary tumour subsets across multiple different cancer types.
Each cell line was genetically characterised through a series of high-throughput analyses at the Broad Institute, including global RNA expression patterns, changes in DNA copy number, as well as DNA sequence variations in genes associated with cancer, and pharmacologic profiling for several drugs in about half of the cell lines. Algorithms were developed to predict drug responses based on the genetic and molecular make-up of cancer cells.
A computational challenge met by adapting algorithms to the biological data
Correlating the more than 50,000 genetic and molecular features that emerged from these cell lines created a computational challenge that the scientists met by adapting algorithms to the biological data. They tested this tool against genetic alterations known to predict sensitivity to cancer drugs, and confirmed the value of their systematic approach. Then they applied the predictive modelling methodology to genetic subtypes of cancer known to pose challenges for current treatment modalities.
For example, a variety of cancers have mutations in the NRAS gene, which activates signalling pathways important in tumour growth. Some NRAS-mutant cancers, including a subset of melanomas, may prove vulnerable to drugs that block a MEK, which is also involved in signalling. The scope of the Cancer Cell Line Encyclopaedia enabled the investigators to study approximately 40 cancer cell lines with this mutation to see if they could predict sensitivity to MEK inhibitor drugs, some of which are being studied in clinical trials.
One of the genetic features that rose to the top of the analysis was expression of the aryl hydrocarbon receptor (AHR) gene in cell lines that were highly sensitive to MEK inhibitors. This suggested that high levels of AHR may indicate higher sensitivity to MEK inhibitor drugs. Additional experiments suggested that some of these same cell lines might also depend on AHR activity, and that MEK inhibitors might simultaneously intercept AHR function in some instances.
The scientists also found new predictors of sensitivity to existing chemotherapy drugs in other cancer cell lines. Elevated levels of SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Another analysis indicates that multiple myeloma may respond to IGF1 receptor inhibitors. Formal clinical studies will be required to learn if these features will hold true in patients.
Armed with this kind of knowledge from the Cancer Cell Line Encyclopaedia, researchers may have a much clearer idea of which tumours are most likely to respond to particular drugs before using them in clinical trials. Knowing that kind of information very early might help to improve the success rate of drug development, compared to a genetically ’agnostic’ approach that includes any patient with advanced cancer without knowledge of a genetic profile. In addition, the scientists can ask questions not only about emerging targeted therapies, but also about standard chemotherapy drugs.
Data publicly available
There are more volumes to be written in this encyclopaedia. From a computational biology perspective, it’s a clean, complex data set that allows many more analyses. In the next phase, analyses based on deeper sequencing, profiles of metabolic activity, and epigenetic modifications will also be added.
Pairing this information with ways to rapidly genotype patient tumour samples represents the next step in the effort to enable the personalization of cancer treatment. Some major research hospitals already routinely genetically profile cancer patients’ tumours, and many more are likely to follow, according to the researchers.
Probing cell lines with medicines targeted at specific pathways provides a powerful tool for design of cancer treatment. The researchers place this information in the public domain with hope that many in industry and academia will use these data to discover new drug targets, to evaluate current therapies, and to facilitate treatment for their patients with cancer. The Cancer Cell Line Encyclopaedia is available online at www.broadinstitute.org/ccle.
The Cancer Cell Line Encyclopaedia project was enabled by a grant from the Novartis Institutes for Biomedical Research. Additional funding support was provided by the National Cancer Institute (USA), the Starr Cancer Consortium, and the NIH Director’s New Innovator Award.