Cancer Therapeutics Response Portal: A CTD² Network Resource for Mining Candidate Cancer Dependencies
In the process of malignancy, cells acquire multiple genetic alterations, such as focal mutations, translocations, amplifications, or deletions, which allow them to survive and divide uncontrollably. In addition to these genetic changes, normal functioning proteins that play roles in innocuous cellular processes may become essential to cancer cell survival when they get co-opted into partnerships with oncogene proteins. The latter is a phenomenon known as non-oncogene addiction or oncogene-induced dependency. Insights into cancer genomes have led to novel clinical therapies that target oncogene proteins, and such targeted therapies have yielded high patient response rates. However, less than one percent of patients suffering from cancer benefit from these therapies, and patient-matched therapies that target essential non-oncogenic proteins or their related pathways have been difficult to determine. To accelerate discovery of patient-matched therapies, systematic approaches are needed to identify oncogene-induced dependencies that cancers acquire as a result of specific cellular or genetic features and small-molecule drugs that target them.
As part of the National Cancer Institute’s Cancer Target Discovery and Development (CTD2) Network, we profiled a large number of human cancer cell lines using a novel “Informer Set” of more than 350 small molecules to reveal such dependencies and their inhibitors. We deposited the dataset into a publicly accessible data portal, called the Cancer Therapeutics Response Portal (CTRP; www.broadinstitute.org/ctrp; Figure 1), so that other researchers can make connections between the genetic and lineage features of cancer cell lines and small-molecule sensitivities. The CTRP, along with findings from an initial analysis, are described in a recent publication (https://pubmed.ncbi.nlm.nih.gov/23993102/)1.
Figure 1. The Cancer Therapeutics Response Portal (CTRP; http://portals.broadinstitute.org/ctrp/ ) homepage (top left) allows users to access data through three entry points: small molecules, enriched features, or targets. Once inside the portal, users can mine this resource for novel and therapeutically exploitable vulnerabilities in different cancer types across ~185 small molecules. For example, a user might enter through the “Compounds” link then search for a molecule of interest, like navitoclax. Under the “General” tab, the navitoclax entry shows the chemical structure of the compound and provides other general information (top right). Under the “Enrichment Analysis” tab, users can select or exclude specific cell line subtypes and datasets. For example, CTNNB1-mutant cancer cell lines are sensitive to navitoclax (bottom).
Historically, human cancer cell lines derived from many different types of tumors have been used in high-throughput profiling efforts to reveal patterns of small-molecule sensitivities. These studies have been limited in the number, diversity, or level of molecular characterization of cancer cell lines or small molecules used. One of the earliest profiling efforts, the NCI-602 probed a limited set of 59 cancer cell lines from various lineages with more than 100,000 diverse small molecules and identified mostly lineage-specific small-molecule sensitivities. More recently, the Cancer Cell Line Encyclopedia (CCLE), a joint effort between the Broad Institute and Novartis Institutes for BioMedical Research, profiled 479 cancer cell lines with significant genomic characterization using 24 anti-cancer drugs3. Massachusetts General Hospital and the Wellcome Trust Sanger Institute profiled 350 cancer cell lines against 130 pre-clinical or clinical anti-cancer agents, though the set of genomic alterations correlated to sensitivity was limited to ~70 genes4. The Lawrence Berkeley National Laboratory and MD Anderson Cancer Center profiled 77 therapeutic compounds against a panel of ~50 breast cancer cell lines and linked various subsets, pathways and genetic features of these lines to drug sensitivity5.
By leveraging work from previous projects and recent advances in high-throughput technologies, we addressed previous shortcomings of high-throughput cancer profiling studies. We used 242 of ~1000 cancer cell lines from the CCLE, which had been genomically characterized for gene expression, amplification/deletion, and somatic mutation in 1,645 cancer genes, and had lineage or histology annotations. These cell lines were established from many different types of tumors. Furthermore, we used an Informer Set of 354 small molecules comprising 35 FDA-approved drugs, 54 clinical candidates, and 266 probes – tool compounds used in biological research to help illuminate pathways and mechanisms of protein function. These probes and drugs each selectively target distinct nodes in cancer cell circuitry and collectively modulate a broad array of cellular processes. Each cell line was grown in its preferred media, and each compound tested at eight different concentrations, with the starting concentration of each compound selected based on literature review. We generated concentration-response curves that determined each compound’s potency and efficacy for each cell line. The area under the concentration-response curve was used to calculate compound (i.e., small-molecule) sensitivities. We input the resulting data as well as previously determined molecular characterizations/annotations into one database and created the CTRP for access, query, and visualization.
The current version of CTRP (Figure 1) uses pre-computed visualizations to display more than 75,000 statistically significant connections between 185 small molecules and cancer cell lines of a particular lineage or with particular mutations. The portal also allows users to customize the query by eliminating cancer cell line subtypes, such as frequently sensitive or highly mutated ones, from analyses. Specific details on how to make novel connections between molecular features of cancer cell lines and the sensitivity profiles are found in the corresponding publication (https://pubmed.ncbi.nlm.nih.gov/23993102/)1. Of note, researchers may use the CTRP to identify drugs that could rapidly be tested clinically, because FDA-approved drugs and clinical candidates are included the Informer Set. This critical new resource may aid in advancing discovery of potential cancer drugs matched to the patient populations most likely to benefit from them.
The CTRP is a living resource that will grow over time. We recently completed a second phase of new small-molecule sensitivity data collection. This phase expanded the number of genetically characterized cancer cell lines to ~900 and the Informer Set to ~545 molecules. The new, enlarged Informer Set not only targets a larger swath of proteins, but also includes novel chemical entities, such as stapled-helical peptides, more FDA-approved drugs and clinical candidates, and rational combinations of small molecules. We are starting to plan a new generation of CTRP to accompany our new cancer cell-line profiling datasets. In particular, we anticipate three functionalities in a future CTRP version: (1) application of single-feature lineage or mutation enrichment analyses to new datasets with improved computational methods; (2) single-gene correlation analyses between small-molecule sensitivity and gene-expression or copy-number features; and (3) compound and cell-line correlation and clustering analyses using small-molecule sensitivity data alone. We are also developing additional methods to analyze and visualize the data. For example, we are incorporating the ability to correlate small-molecule sensitivity to combinations of features or to genetic vulnerabilities uncovered by Project Achilles6, another CTD2-sponsored initiative that uses a genome-wide shRNA library to silence individual genes and identify those genes that affect cell survival.
We strongly encourage the scientific community to use the CTRP to mine for novel and therapeutically exploitable vulnerabilities in different cancer types. Many have already started using this hypothesis-generating tool1. Preliminary user-trend analysis for the month of January indicates about 32 users log in per day, with each user viewing an average of >170 results. We hope to attract more users as we increase the inputs, features, and functionality of this resource. As the volume and diversity of data in publicly available resources like the CTRP continue to grow, the catalog of cancer genes and related dependencies as well as the drugs that target them will likewise grow. Thus, we anticipate the ability to match patients with potentially effective drugs and that improved patient outcomes will dramatically increase.
- Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al. (2013) An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154(5):1151-61 (PMID: 23993102)
- Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer 6(10):813-23 (PMID: 16990858)
- Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603-7 (PMID: 22460905)
- Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. (2012) Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483(7391):570-5 (PMID: 22460902)
- Heiser LM, Sadanandam A, Kuo WL, Benz SC, Goldstein TC, Ng S, et al. (2012) Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc Natl Acad Sci USA 109(8):2724-9 (PMID: 22003129)
- Cheung HW, Cowley GS, Weir BA, Boehm JS, Rusin S, Scott JA, et al. (2011) Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc Natl Acad Sci USA 108(30):12372-7 (PMID: 21746896)
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