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How to Cure Cancer in 3 Easy Steps

Mar 6, 2017 6:10:15 PM No Comments
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When I have read articles just like this one early on in my career, I would laugh and categorize it with blogs regarding Bigfoot and the Loch Ness Monster. However, much has changed in the past 10 years. New technologies have been developed and milestones have been reached that should have Cancer a little worried. These 3 steps might be viewed to some as obvious, but I argue that it’s how the researchers have utilized the technology wisely that has made the difference. I have identified some papers that have carved a successful path to Cancer's possible demise.

1. Integrating in vitro and in vivo systems to identify targets using a pooled approach

Since the successful mapping of the human genome in 2003, there has been a major campaign taking aim at mapping the genome of the over 200 types of human cancer. With the maps in hand and using other tools such as microarrays and next generation sequencing (NGS), researchers have identified copy number alterations (CNA’s) resulting in a list of candidate oncogenes and tumor suppressors.  The major difficulty is combing through the candidates to identify driver mutations hiding amongst the passenger mutations.  The subsequent validation of these suspected oncogenes and tumor suppressors as responsible for tumorigenesis is no small endeavor, especially if it’s a long list of candidates.  Below are a couple of articles that have illustrated how to use a pooled approach in order to identify driver mutations, and I have listed the common key elements of these articles:

  • Isolate the cell of origin of the particular cancer
  • Engineer the isolated cells to express the candidate oncogenes or shRNA/CRISPR to knockdown expression of candidate tumor suppressors
  • Inoculate immunodeficient mice with the engineered cells and monitor for tumor development.

An in vivo screen identifies ependymoma oncogenes and tumor-suppressor genes. K.M. Mohankumar, D.S. Currle, E. White, N. Boulos, J. Dapper, C. Eden, B. Nimmervoll, R. Thiruvenkatam, M. Connelly, T.A. Kranenburg, G. Neale, S. Olsen, Y.D. Wang, D. Finkelstein, K. Wright, K. Gupta, D.W. Ellison, A.O. Thomas and R.J. Gilbertson. (2016) Nature Genetics 47(8):878-87

An Oncogenomics-Based In Vivo RNAi Screen Identifies Tumor Suppressors in Liver Cancer. Zender, L., Xue, W., Zuber, J., Semighini, C. P., Krasnitz, A., Ma, B., Zender, P., Kubicka, S., Luk, J. M., Schirmacher, P., McCombie, W. R., Wigler, M. H., Hicks, J. B., Hannon, G. J., Powers, S., Lowe, S. W. (2008) Cell, 135 (5). pp. 852-864. ISSN 0092-8674

2. High throughput drug screening to identify hits

Once targets have been identified, the next logical step is to screen your compound library for activity against that target. It is useful to use cell lines of known expression and gene dose of your target. The Cancer Cell Line Encyclopedia provides genome information for more than 1,000 cancer cell lines and can be mined for expression profiles. Additionally, isogenic cell lines provide an excellent outlet for controlling gene expression and measuring gene effects. These engineered cells provide both a control (lacking the mutation) and the mutant cell line. Following initial primary screens with your single agent compound library, combination screening can reveal robust synergies and antagonisms that can enhance efficacy and response rate at defined pathways. See below for key papers that used combination approaches.

Key elements of articles:

  • Usage of well characterized cell lines with known sensitivities and/or generation of isogenic pairs
  • Analyzing synergies of rational, well-justified combinations
  • Well-defined reference compounds for MOA analysis

MDM2 antagonists synergize broadly and robustly with compounds targeting fundamental oncogenic signaling pathways. Saiki AY, Caenepeel S, Yu D, Lofgren JA, Osgood T, Robertson R, Canon J, Su C, Jones A, Zhao X, Deshpande C, Payton M, Ledell J, Hughes PE, Oliner JD. Oncotarget. 2014 Apr 30;5(8):2030-43.

Inhibiting Tankyrases sensitizes KRAS-mutant cancer cells to MEK inhibitors via FGFR2 feedback signaling. Schoumacher M, Hurov KE1, Lehár J, Yan-Neale Y, Mishina Y, Sonkin D, Korn JM, Flemming D, Jones MD1, Antonakos B1, Cooke VG, Steiger J, Ledell J, Stump MD, Sellers WR, Danial NN, Shao W. Cancer Res. 2014 Jun 15;74(12):3294-305

Adenosine A2A and beta-2 adrenergic receptor agonists: novel selective and synergistic multiple myeloma targets discovered through systematic combination screening. Rickles RJ, Tam WF, Giordano TP 3rd, Pierce LT, Farwell M, McMillin DW, Necheva A, Crowe D, Chen M, Avery W, Kansra V, Nawrocki ST, Carew JS, Giles FJ, Mitsiades CS, Borisy AA, Anderson KC, Lee MS.  Mol Cancer Ther. 2012 Jul;11(7):1432-42.

3. Using high fidelity PDX models for preclinical efficacy tests to validate hits in vivo

Preclinical efficacy testing is last but absolutely not the least of the 3 steps. It’s important to understand the complexity and heterogeneity of tumor histology when considering an in vivo model to screen your compound(s) against. Patient Derived Xenografts (PDX) models best represent the heterogeneity of cancer in contrast to Cell-line Derived Xenografts (CDX) that are propagated in artificial conditions.  I have created some guidelines when choosing a PDX model in a previous Blog titled: “Why Highly Characterized PDX Cancer Models Are Important for Targeted Therapy” I have also identified a couple of groups that have created well characterized PDX models, that maintain high fidelity and have extensive patient data. Additionally, the researchers also confirm that the models recapitulate the treatment response of the patient.

Key Elements of articles:

  • Extensive genomic analysis to validate models are stable and genetic signatures are still present
  • Extensive patient history profile
  • Validate that the models show the same response as the patient.

Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. Li S, Shen D, Shao J, Crowder R, Liu W, Prat A, He X, Liu S, Hoog J, Lu C, Ding L, Griffith OL, Miller C, Larson D, Fulton RS, Harrison M, Mooney T, McMichael JF, Luo J, Tao Y, Goncalves R, Schlosberg C, Hiken JF, Saied L, Sanchez C, Giuntoli T, Bumb C, Cooper C, Kitchens RT, Lin A, Phommaly C, Davies SR, Zhang J, Kavuri MS, McEachern D, Dong YY, Ma C, Pluard T, Naughton M, Bose R, Suresh R, McDowell R, Michel L, Aft R, Gillanders W, DeSchryver K, Wilson RK, Wang S, Mills GB, Gonzalez-Angulo A, Edwards JR, Maher C, Perou CM, Mardis ER, Ellis MJ. (2013) Cell Reports; Sep 26;4(6):1116-30

Personalized Preclinical Trials in BRAF Inhibitor–Resistant Patient-Derived Xenograft Models Identify Second-Line Combination Therapies. Krepler, Clemens; Xiao, Min; Sproesser, Katrin; Brafford, Patricia A; Shannan, Batool; Beqiri, Marilda; Liu, Qin; Xu, Wei; Garman, Bradley; Nathanson, Katherine L; Xu, Xiaowei; Karakousis, Giorgos C; Mills, Gordon B; Lu, Yiling; Ahmed, Tamer A; Poulikakos, Poulikos I; Caponigro, Giordano; Boehm, Markus; Peters, Malte; Schuchter, Lynn M; Weeraratna, Ashani T; Herlyn, Meenhard. Clin Cancer Res; 22(7): 1592-602, 2016 Apr 01.


Although this isn’t a magical step-by-step “how to cure cancer” guide as the title indicates, the purpose of this article was to point out a smart path that might be used to identify promising therapeutics against one of the leading causes of death in the world. Horizon Discovery offers a one stop shop for gene editing, high throughput screening and PDX modeling.

PDX models CRISPR Screening Drug Combinations

Contributing Authors:

Chris Eden, Ph.D

Sarah Hardison, Ph.D

#Cell lines, #Gene editing, #CRISPR, #Oncology Panels, #Target identification, #Target validation, #PDX, #Cancer models, #Preclinical efficay testing, #High Fidelity Models

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