An in-silico approach to predict and exploit synthetic lethality in cancer metabolism

Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly available genome-scale loss-of-function screens, a possible mechanism by which the inhibition of RRM1 is effective in cancer is established. Overall, our approach shows promising results and lays the foundation to build a novel family of algorithms to target metabolism in cancer.Exploiting synthetic lethality is a promising approach for cancer therapy. Here, the authors present an approach to identifying such interactions by finding genetic minimal cut sets (gMCSs) that block cancer proliferation, and apply it to study the lethality of RRM1 inhibition in multiple myeloma.

[1]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[2]  Cole Trapnell,et al.  Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. , 2010, Nature biotechnology.

[3]  T. Hagemann,et al.  The MEK1/2 Inhibitor Pimasertib Enhances Gemcitabine Efficacy in Pancreatic Cancer Models by Altering Ribonucleotide Reductase Subunit-1 (RRM1) , 2015, Clinical Cancer Research.

[4]  Francisco J. Planes,et al.  Direct calculation of minimal cut sets involving a specific reaction knock-out , 2016, Bioinform..

[5]  Eytan Ruppin,et al.  Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer , 2014, eLife.

[6]  G. Marcucci,et al.  RNA-dependent inhibition of ribonucleotide reductase is a major pathway for 5-azacytidine activity in acute myeloid leukemia. , 2012, Blood.

[7]  S. Mousses,et al.  Identification of molecular vulnerabilities in human multiple myeloma cells by RNA interference lethality screening of the druggable genome. , 2012, Cancer research.

[8]  D. Silver,et al.  Synthetic lethality--a new direction in cancer-drug development. , 2009, The New England journal of medicine.

[9]  Matthew N. McCall,et al.  The Gene Expression Barcode 3.0: improved data processing and mining tools , 2013, Nucleic Acids Res..

[10]  R. Weiss,et al.  Ribonucleotide reductase and cancer: biological mechanisms and targeted therapies , 2014, Oncogene.

[11]  Alan Ashworth,et al.  Synthetic lethal approaches to breast cancer therapy , 2010, Nature Reviews Clinical Oncology.

[12]  Bernhard O. Palsson,et al.  BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions , 2010, BMC Bioinformatics.

[13]  Paul Flicek,et al.  Whole-epigenome analysis in multiple myeloma reveals DNA hypermethylation of B cell-specific enhancers , 2015, Genome research.

[14]  Rafael A Irizarry,et al.  Frozen robust multiarray analysis (fRMA). , 2010, Biostatistics.

[15]  Ines Thiele,et al.  Computationally efficient flux variability analysis , 2010, BMC Bioinformatics.

[16]  Steffen Klamt,et al.  Minimal cut sets in biochemical reaction networks , 2004, Bioinform..

[17]  Angel Rubio,et al.  Computing the shortest elementary flux modes in genome-scale metabolic networks , 2009, Bioinform..

[18]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[19]  J. Nielsen,et al.  Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling , 2014, Molecular systems biology.

[20]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[21]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[22]  P. Reichard,et al.  Ribonucleotide reductases. , 1998, Annual review of biochemistry.

[23]  Karthik M. Kodigepalli,et al.  Regulation of deoxynucleotide metabolism in cancer: novel mechanisms and therapeutic implications , 2015, Molecular Cancer.

[24]  T. Matsebatlela,et al.  3,4-Dihydroxy-benzohydroxamic acid (Didox) suppresses pro-inflammatory profiles and oxidative stress in TLR4-activated RAW264.7 murine macrophages. , 2015, Chemico-biological interactions.

[25]  Bronwen L. Aken,et al.  GENCODE: The reference human genome annotation for The ENCODE Project , 2012, Genome research.

[26]  L. Cantley,et al.  Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation , 2009, Science.

[27]  Ellen T. Gelfand,et al.  Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies , 2014, Scientific Data.

[28]  Santhosh K. P. Kumar,et al.  Didox, a ribonucleotide reductase inhibitor, induces apoptosis and inhibits DNA repair in multiple myeloma cells , 2006, British journal of haematology.

[29]  Lior Pachter,et al.  Sequence Analysis , 2020, Definitions.

[30]  Daniel Machado,et al.  Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism , 2014, PLoS Comput. Biol..

[31]  Pablo Tamayo,et al.  ATARiS: Computational quantification of gene suppression phenotypes from multisample RNAi screens , 2013, Genome research.

[32]  J. Moffat,et al.  Measuring error rates in genomic perturbation screens: gold standards for human functional genomics , 2014, bioRxiv.

[33]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.

[34]  Gabriela Kalna,et al.  Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase , 2011, Nature.

[35]  Soyoung Lee,et al.  Synthetic lethal metabolic targeting of cellular senescence in cancer therapy , 2013, Nature.

[36]  Minoru Kanehisa,et al.  KEGG as a reference resource for gene and protein annotation , 2015, Nucleic Acids Res..

[37]  Steffen Klamt,et al.  Minimal cut sets in a metabolic network are elementary modes in a dual network , 2012, Bioinform..

[38]  Ali R. Zomorrodi,et al.  Genome-scale gene/reaction essentiality and synthetic lethality analysis , 2009, Molecular systems biology.

[39]  J. Cuezva,et al.  Post-transcriptional regulation of the mitochondrial H(+)-ATP synthase: a key regulator of the metabolic phenotype in cancer. , 2011, Biochimica et biophysica acta.

[40]  Steffen Klamt,et al.  Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks , 2014, PLoS Comput. Biol..

[41]  Francisco J. Planes,et al.  Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction Method , 2016, PloS one.

[42]  S. Klamt,et al.  Generalized concept of minimal cut sets in biochemical networks. , 2006, Bio Systems.

[43]  Yun Yen,et al.  Ribonucleotide Reductase Large Subunit M1 Predicts Poor Survival Due to Modulation of Proliferative and Invasive Ability of Gastric Cancer , 2013, PloS one.