The advancements of genomics and data integration in sarcoma research

Ricardo J. Flores, Aaron J. Kelly, Manjula Nakka, Xiang Chen, Jiayi Sun, Tsz-Kwong Man


In the age of big data, genomics and clinical research have reached a crossroads. A wealth of data is being generated, but it is becoming increasingly complicated to analyze these data to extract meaningful results. The ability to understand biological systems holistically has unprecedented potential to transform how cancers are treated. Recent major advances leading biomedical research towards “systems medicine” have been fueled by high-throughput platforms, such as microarrays and next-generation sequencing, which can capture vast amounts of data in different genomic spaces. Unfortunately, because of high dimensionality and complex relationships among these data, inferring comprehensive and useful biological models has proven computationally and statistically challenging. However, novel bioinformatic methods for data integration of cancer genomic datasets have been developed. In this review, we will describe the applications of various genomic approaches in sarcoma research and introduce bioinformatic methods for data integration. With the continuing evolution of technological and bioinformatic methodologies, the application of big data within clinics and hospitals will ultimately result in significant improvements on how cancers are detected and treated.


omics; genomics; sarcomas; sequencing; microarray; data integration; bioinformatics; big data

Full Text:



Boveri T. Concerning the origin of malignant tumours by Theodor Boveri. Translated and annotated by Henry Harris. J Cell Sci 2008; 121(Suppl 1): 1–84. doi: 10.1242/jcs.025742

Bombonati A, Sgroi DC. The molecular pathology of breast cancer progression. J Pathol 2011; 223(2): 307–317. doi: 10.1002/path.2808

Krizman DB, Wagner L, Lash A, Strausberg RL, Emmert-Buck MR. The Cancer Genome Anatomy Project: EST sequencing and the genetics of cancer progression. Neoplasia 1999; 1(2): 101–106. doi: 10.1038/sj.neo.7900002

Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature 2009; 458(7239): 719–724. doi: 10.1038/nature07943

Tsao H, Chin L, Garraway LA, Fisher DE. Melanoma: From mutations to medicine. Genes Dev 2012; 26(11): 1131–1155. doi: 10.1101/gad.191999.112

Stephens PJ, Tarpey PS, Davies H, Loo PV, Greenman C. The landscape of cancer genes and mutational processes in breast cancer. Nature 2012; 486(7403): 400–404. doi: 10.1038/nature11017

Weinstein JN, Collisson EA, Mills GB, Mills Shaw KR, Ozenberger BA, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013; 45(10): 1113–1120. doi: 10.1038/ng.2764

Pugh TJ, Morozova O, Attiyeh EF, Asgharzadeh S, Wei JS, et al. The genetic landscape of high-risk neuroblastoma. Nat Genet 2013; 45(3): 279–284. doi: 10.1038/ng.2529

Pfeifer JD. Clinical next generation sequencing in cancer. Cancer Genet 2013; 206(12): 409–412. doi: 10.1016/j.cancergen.2013.10.004

Ho CC, Mun KS, Naidu R. SNP array technology: An array of hope in breast cancer research. Malays J Pathol 2013; 35(1): 33–43.

Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, et al. Cancer genome landscapes. Science 2013; 339(6127): 1546–1558. doi: 10.1126/science.1235122

Kawakami H, Zaanan A, Sinicrope FA. Microsatellite instability testing and its role in the management of colorectal cancer. Curr Treat Opt Oncol 2015; 16(7): 30. doi: 10.1007/s11864-015-0348-2

Iyevleva AG, Imyanitov EN. Cytotoxic and targeted therapy for hereditary cancers. Hered Cancer Clin Pract 2016; 14(1): 17. doi: 10.1186/s13053-016-0057-2

Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med 2016; 375(19): 1823–1833. doi: 10.1056/NEJMoa1606774

Man TK, Lu X-Y, Jaeweon K, Perlaky L, Harris CP, et al. Genome-wide array comparative genomic hybridization analysis reveals distinct amplifications in osteosarcoma. BMC Cancer 2004; 4: 45. doi: 10.1186/1471-2407-4-45

Savola S, Klami A, Tripathi A, Niini T, Serra M, et al. Combined use of expression and CGH arrays pinpoints novel candidate genes in Ewing sarcoma family of tumors. BMC Cancer 2009; 9: 17. doi: 10.1186/1471-2407-9-17

de Leeuw N, Hehir-Kwa JY, Simons A, Geurts van Kessel A, Smeets DF, et al. SNP array analysis in constitutional and cancer genome diagnostics: Copy number variants, genotyping and quality control. Cytogenet Genome Res 2011; 135(3–4): 212–221. doi: 10.1159/000331273

Lynn M, Wang Y, Slater J, Shah N, Conroy J, et al. High-resolution genome-wide copy-number analyses identify localized copy-number alterations in Ewing sarcoma. Diagn Mol Pathol 2013; 22(2): 76–84. doi: 10.1097/PDM.0b013e31827a47f9

Chudasama P, Renner M, Straub M, Mughal SS, Hutter B, et al. Targeting fibroblast growth factor receptor 1 for treatment of soft-tissue sarcoma. Clin Cancer Res 2016; 23(4): 962–973. doi: 10.1158/1078-0432.CCR-16-0860

Paniz-Mondolfi AE, Jour G, Johnson M, Reidy J, Cason RC, et al. Primary cutaneous carcinosarcoma: Insights into its clonal origin and mutational pattern expression analysis through next-generation sequencing. Hum Pathol 2013; 44(12): 2853–2860. doi: 10.1016/j.humpath.2013.07.014

Mosquera JM, Sboner A, Zhang L, Kitabayashi N, Chen CL, et al. Recurrent NCOA2 gene rearrangements in congenital/infantile spindle cell rhabdomyosarcoma. Genes Chromosomes Cancer 2013; 52(6): 538–550. doi: 10.1002/gcc.22050

Szuhai, K., de Jong D, Leung WY, Fletcher CD, Hogendoorn PC. Transactivating mutation of the MYOD1 gene is a frequent event in adult spindle cell rhabdomyosarcoma. J Pathol 2014; 232(3): 300–307. doi: 10.1002/path.4307

Seki M, Nishimura R, Yoshida K, Shimamura T, Shiraishi Y, et al. Integrated genetic and epigenetic analysis defines novel molecular subgroups in rhabdomyosarcoma. Nat Commun 2015; 6: 7557. doi: 10.1038/ncomms8557

Jour G, Scarborough JD, Jones RL, Loggers E, Pollack SM, et al. Molecular profiling of soft tissue sarcomas using next-generation sequencing: A pilot study toward precision therapeutics. Hum Pathol 2014; 45(8): 1563–1571. doi: 10.1016/j.humpath.2014.04.012

Chen X, Bahrami A, Pappo A, Easton J, Dalton J, et al. Recurrent somatic structural variations contribute to tumorigenesis in pediatric osteosarcoma. Cell Rep 2014; 7(1): 104–112. doi: 10.1016/j.celrep.2014.03.003

Forment JV, Kaidi A, Jackson S. Chromothripsis and cancer: Causes and consequences of chromosome shattering. Nat Rev Cancer 2012; 12(10): 663–670. doi: 10.1038/nrc3352

Stephens PJ, Greenman CD, Fu B, Yang F, Bignell GR, et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 2011; 144(1): 27–40. doi: 10.1016/j.cell.2010.11.055

Baylin SB, Herman JG. DNA hypermethylation in tumorigenesis: Epigenetics joins genetics. Trends Genet 2000; 16(4): 168–174. doi: 10.1016/S0168-9525(99)01971-X

Bird AP. CpG-rich islands and the function of DNA methylation. Nature 1986; 321(6067): 209–213. doi: 10.1038/321209a0

Cedar H, Bergman Y. Linking DNA methylation and histone modification: Patterns and paradigms. Nat Rev Genet 2009; 10(5): 295–304. doi: 10.1038/nrg2540

Bujko M, Kowalewska M, Danska-Bidzinska A, Bakula-Zalewska E, Siedecki J, et al. The promoter methylation and expression of the O6-methylguanine-DNA methyltransferase gene in uterine sarcoma and carcinosarcoma. Oncol Lett 2012; 4(3): 551–555. doi: 10.3892/ol.2012.771

Jakob J, Hille M, Sauer C, Ströbel P, Wenz F, et al. O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a rare event in soft tissue sarcoma. Radiat Oncol 2012; 7: 180. doi: 10.1186/1748-717X-7-180

Avigad S, Shukla S, Naumov I, Cohen IJ, Ash S, et al. Aberrant methylation and reduced expression of RASSF1A in Ewing sarcoma. Pediatr Blood Cancer 2009; 53(6): 1023–1028. doi: 10.1002/pbc.22115

Nestheide S, Bridge JA, Barnes M, Frayer R, Sumegi J. Pharmacologic inhibition of epigenetic modification reveals targets of aberrant promoter methylation in Ewing sarcoma. Pediatr Blood Cancer 2013; 60(9): 1437–1446. doi: 10.1002/pbc.24526

Patel N, Black J, Chen X, Marcondes AM, Grady WM, et al. DNA methylation and gene expression profiling of ewing sarcoma primary tumors reveal genes that are potential targets of epigenetic inactivation. Sarcoma 2012; 2012: 498472. doi: 10.1155/2012/498472

Ueno H, Okita H, Akimoto S, Kobayashi K, Nakabayashi K, et al. DNA methylation profile distinguishes clear cell sarcoma of the kidney from other pediatric renal tumors. PLoS One 2013; 8(4): e62233. doi: 10.1371/journal.pone.0062233

Renner M, Wolf T, Meyer H, Hartmann W, Penzel R, et al. Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas. Genome Biol 2013; 14(12): r137. doi: 10.1186/gb-2013-14-12-r137

Ulaner GA, Vu TH, Li T, Hu JF, Yao XM, et al. Loss of imprinting of IGF2 and H19 in osteosarcoma is accompanied by reciprocal methylation changes of a CTCF-binding site. Hum Mol Genet 2003; 12(5): 535–549. doi: 10.1093/hmg/ddg034

Yamada KM, Araki M. Tumor suppressor PTEN: Modulator of cell signaling, growth, migration and apoptosis. J Cell Sci 2001; 114(Pt 13): 2375–2382.

Lu, C., Venneti S, Akalin A, Fang F, Ward PS, et al. Induction of sarcomas by mutant IDH2. Genes Dev 2013; 27(18): 1986–1998. doi: 10.1101/gad.226753.113

Bos PD, Zhang XH-F, Nadal C, Shu Wp, Gomis RR, et al. Genes that mediate breast cancer metastasis to the brain. Nature 2009; 459(7249): 1005–1009. doi: 10.1038/nature08021

Leonard P, Sharp T, Henderson S, Hewitt D, Pringle J, et al. Gene expression array profile of human osteosarcoma. Br J Cancer 2003. 89(12): 2284–2288. doi: 10.1038/sj.bjc.6601389

Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, et al. Genes that mediate breast cancer metastasis to lung. Nature 2005; 436(7050): 518–524. doi: 10.1038/nature03799

Taylor BS, Barretina J, Maki RG, Antonescu CR, Singer S, et al. Advances in sarcoma genomics and new therapeutic targets. Nat Rev Cancer 2011; 11(8): 541–557. doi: 10.1038/nrc3087

Khanna C, Khan J, Nguyen P, Prehn J, Caylor J, et al. Metastasis-associated differences in gene expression in a murine model of osteosarcoma. Cancer Res 2001; 61(9): 3750–3759.

Bulut G, Hong SH, Chen K, Beauchamp EM, Rahim S, et al. Small molecule inhibitors of ezrin inhibit the invasive phenotype of osteosarcoma cells. Oncogene 2012; 31(3): 269–281. doi: 10.1038/onc.2011.245

Celik H, Bulut G, Han J, Graham GT, Minas TZ, et al. Ezrin inhibition up-regulates stress response gene expression. J Biol Chem 2016; 291(25): 13257–13270. doi: 10.1074/jbc.M116.718189

Man TK, Chintagumpala M, Visvanathan J, Shen J, Perlaky L, et al. Expression profiles of osteosarcoma that can predict response to chemotherapy. Cancer Res 2005; 65(18): 8142–8150. doi: 10.1158/0008-5472.CAN-05-0985

Grunewald TG, Willier S, Janik D, Unland R, Reiss C, et al. The Zyxin-related protein thyroid receptor interacting protein 6 (TRIP6) is overexpressed in Ewing’s sarcoma and promotes migration, invasion and cell growth. Biol Cell 2013; 105(11): 535–547. doi: 10.1111/boc.201300041

Town J, Pais H, Harrison S, Stead LF, Bataille C, et al. Exploring the surfaceome of Ewing sarcoma identifies a new and unique therapeutic target. Proc Natl Acad Sci U S A 2016; 113(13): 3603–3608. doi: 10.1073/pnas.1521251113

Sankar S, Theisen ER, Bearss J, Mulvihill T, Hoffman LM, et al. Reversible LSD1 inhibition interferes with global EWS/ETS transcriptional activity and impedes Ewing sarcoma tumor growth. Clin Cancer Res 2014; 20(17): 4584–4597. doi: 10.1158/1078-0432.CCR-14-0072

Pierron G, Tirode F, Lucchesi C, Reynaud S, Ballet S, et al. A new subtype of bone sarcoma defined by BCOR–CCNB3 gene fusion. Nat Genet 2012; 44(4): 461–466. doi: 10.1038/ng.1107

Micci F, Gorunova L, Agostini A, Johannessen LE, Brunetti M, et al. Cytogenetic and molecular profile of endometrial stromal sarcoma. Genes Chromosom Cancer 2016; 55(11): 834–846. doi: 10.1002/gcc.22380

Guttman M, Amit I, Garber M, French C, Lin MF, et al. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals. Nature 2009; 458(7235): 223–227. doi: 10.1038/nature07672

Evans JR, Feng FY, Chinnaiyan AM. The bright side of dark matter: lncRNAs in cancer. J Clin Invest 2016; 126(8): 2775–2782. doi: 10.1172/JCI84421

Min L, Garbutt C, Tu C, Hornicek F, Duan Z, et al. Potentials of long noncoding RNAs (lncRNAs) in sarcoma: From biomarkers to therapeutic targets. Int J Mol Sci 2017; 18(4): 731. doi: 10.3390/ijms18040731

Li Z, Yu X, Shen J. Long non-coding RNAs: Emerging players in osteosarcoma. Tumour Biol 2016; 37(3): 2811–2816.

He RQ, Wei QJ, Tang RX, Chen WJ, Yang X, et al. Prediction of clinical outcome and survival in soft-tissue sarcoma using a ten-lncRNA signature. Oncotarget 2017. doi: 10.18632/oncotarget.18165. In Press.

Bao X, Ren T, Huang Y, Sun K, Wang S, et al. Knockdown of long non-coding RNA HOTAIR increases miR-454-3p by targeting Stat3 and Atg12 to inhibit chondrosarcoma growth. Cell Death Dis 2017; 8(2): e2605. doi: 10.1038/cddis.2017.31

Chen F, Mo J, Zhang L. Long noncoding RNA BCAR4 promotes osteosarcoma progression through activating GLI2-dependent gene transcription. Tumour Biol 2016; 37(10): 13403–13412. doi: 10.1007/s13277-016-5256-y

Zhang CL, Zhu K-P, Shen G-Q, Zhu ZS. A long non-coding RNA contributes to doxorubicin resistance of osteosarcoma. Tumour Biol 2016; 37(2): 2737–2748. doi: 10.1007/s13277-015-4130-7

Marques Howarth M, Simpson D, Ngok SP, Nieves B, Chen R, et al. Long noncoding RNA EWSAT1-mediated gene repression facilitates Ewing sarcoma oncogenesis. J Clin Invest 2014; 124(12): 5275–5290. doi: 10.1172/JCI72124

Xie J, Lin D, Lee DHT, Akunowicz J, Hansen M, et al. Copy number analysis identifies tumor suppressive lncRNAs in human osteosarcoma. Int J Oncol 2017; 50(3): 863–872. doi: 10.3892/ijo.2017.3864

Huntzinger E, Izaurralde E. Gene silencing by microRNAs: Contributions of translational repression and mRNA decay. Nat Rev Genet 2011; 12(2): 99–110. doi: 10.1038/nrg2936

Zhou G, Shi X, Zhang J, Wu S, Zhao J. MicroRNAs in osteosarcoma: From biological players to clinical contributors, a review. J Int Med Res 2013; 41(1): 1–12. doi: 10.1177/0300060513475959

Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer 2006; 6(11): 857–866. doi: 10.1038/nrc1997

Garzon R, Calin GA, Croce CM. MicroRNAs in Cancer. Annu Rev Med 2009; 60: 167–179. doi: 10.1146/

Toiyama Y, Takahashi M, Hur K, Nagasaka T, Tanaka K, et al. Serum miR-21 as a diagnostic and prognostic biomarker in colorectal cancer. J Natl Cancer Inst 2013; 105(12): 849–859. doi: 10.1093/jnci/djt101

Gits CM, van Kuijk PF, Jonkers MBE, Boersma AWM, Smid M, et al. MicroRNA expression profiles distinguish liposarcoma subtypes and implicate miR-145 and miR-451 as tumor suppressors. Int J Cancer 2013; 135(2): 348–361. doi: 10.1002/ijc.28694

Nouraee N, Roosbroeck KV, Vasei M, Semnani S, Samaei NM, et al. Expression, tissue distribution and function of miR-21 in esophageal squamous cell carcinoma. PLoS One 2013; 8(9): e73009. doi: 10.1371/journal.pone.0073009

Song B, Wang Y, Xi Y, Kudo K, Bruheim S, et al. Mechanism of chemoresistance mediated by miR-140 in human osteosarcoma and colon cancer cells. Oncogene 2009. 28(46): 4065–4074. doi: 10.1038/onc.2009.274

Song F, Yang D, Liu B, Guo Y, Zheng H, et al. Integrated microRNA network analyses identify a poor-prognosis subtype of gastric cancer characterized by the miR-200 family. Clin Cancer Res 2013; 20(4): 878–889. doi: 10.1158/1078-0432.CCR-13-1844

De Vito C, Riggi N, Cornaz S, Suvà M-L, Baumer K, et al. A TARBP2-dependent miRNA expression profile underlies cancer stem cell properties and provides candidate therapeutic reagents in Ewing sarcoma. Cancer Cell 2012; 21(6): 807–821. doi: 10.1016/j.ccr.2012.04.023

Franzetti GA, Laud-Duval K, Bellanger D, Stern M, Sastre-Garau X, et al. MiR-30a-5p connects EWS-FLI1 and CD99, two major therapeutic targets in Ewing tumor. Oncogene 2013; 32(33): 3915–3921. doi: 10.1038/onc.2012.403

Ugras S, Brill E, Jacobsen A, Hafner M, Socci ND, et al. Small RNA sequencing and functional characterization reveals MicroRNA-143 tumor suppressor activity in liposarcoma. Cancer Res 2011; 71(17): 5659–5669. doi: 10.1158/0008-5472.CAN-11-0890

Lauvrak SU, Munthe E, Kresse SH, Stratford EW, Namløs HM, et al. Functional characterisation of osteosarcoma cell lines and identification of mRNAs and miRNAs associated with aggressive cancer phenotypes. Br J Cancer 2013; 109(8): 2228–2236. doi: 10.1038/bjc.2013.549

Zhao H, Li M, Li L, Yang X, Lan G, et al. MiR-133b is down-regulated in human osteosarcoma and inhibits osteosarcoma cells proliferation, migration and invasion, and promotes apoptosis. PLoS One 2013; 8(12): e83571. doi: 10.1371/journal.pone.0083571

Yu PY, Balkhi MY, Ladner KJ, Alder H, Yu L, et al. A selective screening platform reveals unique global expression patterns of microRNAs in a cohort of human soft-tissue sarcomas. Lab Invest 2016; 96(4): 481–491. doi: 10.1038/labinvest.2015.168

Wang Y, Zhao W, Fu Q. miR-335 suppresses migration and invasion by targeting ROCK1 in osteosarcoma cells. Mol Cell Biochem 2013; 384(1–2): 105–111. doi: 10.1007/s11010-013-1786-4

Jones KB, Salah Z, Mare SD, Galasso M, Gaudio E, et al. miRNA signatures associate with pathogenesis and progression of osteosarcoma. Cancer Res 2012; 72(7): 1865–1877. doi: 10.1158/0008-5472.CAN-11-2663

van der Deen M, Taipaleenmäki H, Zhang Y, Teplyuk NM, Gupta A, et al. MicroRNA-34c inversely couples the biological functions of the runt-related transcription factor RUNX2 and the tumor suppressor p53 in osteosarcoma. J Biol Chem 2013; 288(29): 21307–21319. doi: 10.1074/jbc.M112.445890

Tellez-Gabriel M, Ory B, Lamoureux F, Heymann M-F, Heymann D. Tumour heterogeneity: The key advantages of single-cell analysis. Int J Mol Sci 2016; 17(12): 2142. doi: 10.3390/ijms17122142

Tsoucas D, Yuan GC. Recent progress in single-cell cancer genomics. Curr Opin Genet Dev 2017; 42: 22–32. doi: 10.1016/j.gde.2017.01.002

Li H, Courtois ET, Sengupta D, Tan Y, Chen KH, et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat Genet 2017; 49(5): 708–718. doi: 10.1038/ng.3818

Wang Y, Waters J, Leung ML, Unruh A, Roh W, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 2014; 512(7513): 155–160. doi: 10.1038/nature13600

Tirosh I, Izar B, Prakadan SM, Wadsworth II MH, Treacy D, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 2016; 352(6282): 189–196. doi: 10.1126/science.aad0501

Hecker N, Stephan C, Mollenkopf HJ, Jung K, Preissner R, et al. A new algorithm for integrated analysis of miRNA-mRNA interactions based on individual classification reveals insights into bladder cancer. PLoS One 2013; 8(5): e64543. doi: 10.1371/journal.pone.0064543

Mo Q, Wang S, Seshan VE, Olshen AB, Schultz N, et al. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc Natl Acad Sci U S A 2013; 110(11): 4245–4250. doi: 10.1073/pnas.1208949110

Seoane JA, Day INM, Gaunt TR, Campbell C. A pathway-based data integration framework for prediction of disease progression. Bioinformatics 2014; 30(6): 838–845. doi: 10.1093/bioinformatics/btt610

Shen R, Mo Q, Schultz N, Seshan VE, Olshen AB, et al. Integrative subtype discovery in glioblastoma using iCluster. PLoS One 2012; 7(4): e35236. doi: 10.1371/journal.pone.0035236

Shen R, Olshen AB, Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 2009; 25(22): 2906–2912. doi: 10.1093/bioinformatics/btp543

Srivastava S, Wang W, Manyam G, Ordonez C, Baladandayuthapani V, et al. Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines. EURASIP J Bioinform Syst Biol 2013; 2013(1): 9. doi: 10.1186/1687-4153-2013-9

Wang W, Baladandayuthapani V, Morris JS, Broom BM, Manyam G, et al. iBAG: Integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics 2013; 29(2): 149–59. doi: 10.1093/bioinformatics/bts655

Huopaniemi I, Suvitaival T, Nikkilä J, Orešič M, Kaski S. Multivariate multi-way analysis of multi-source data. Bioinformatics 2010; 26(12): i391–i398. doi: 10.1093/bioinformatics/btq174

Clarke C, Henry M, Doolan P, Kelly S, Aherne S, et al. Integrated miRNA, mRNA and protein expression analysis reveals the role of post-transcriptional regulation in controlling CHO cell growth rate. BMC Genomics 2012; 13: 656. doi: 10.1186/1471-2164-13-656

Borgwardt KM, Gretton A, Rasch MJ, Kriegel HP, Schölkopf B, et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics 2006; 22(14): e49–e57. doi: 10.1093/bioinformatics/btl242

Eroles P, Bosch A, Pérez-Fidalgo JA, Lluch A. Molecular biology in breast cancer: Intrinsic subtypes and signaling pathways. Cancer Treat Rev 2012; 38(6): 698–707. doi: 10.1016/j.ctrv.2011.11.005

Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, et al. Integrated genomic characterization of endometrial carcinoma. Nature 2013; 497(7447): 67–73. doi: 10.1038/nature12113

Flores RJ, Li Y, Yu A, Shen Jh, Rao PH, et al. A systems biology approach reveals common metastatic pathways in osteosarcoma. BMC Syst Biol 2012; 6: 50. doi: 10.1186/1752-0509-6-50

Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 2010; 26(12): i237–i245. doi: 10.1093/bioinformatics/btq182

Ellis MJ, Ding L, Shen D, Luo Jq, Suman VJ, et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 2012; 486(7403): 353–360. doi: 10.1038/nature11143

The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490(7418): 61–70. doi: 10.1038/nature11412

Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: Implications for targeted therapeutics. Br J Cancer 2013; 108(3): 479–485. doi: 10.1038/bjc.2012.581

Kannry JL, Williams MS. Integration of genomics into the electronic health record: Mapping terra incognita. Genet Med 2013; 15(10): 757–760. doi: 10.1038/gim.2013.102

Kho AN, Rasmussen LV, Connolly JJ, Peissig PL, Starren J, et al. Practical challenges in integrating genomic data into the electronic health record. Genet Med 2013; 15(10): 772–778. doi: 10.1038/gim.2013.131

Manolio TA, Chisholm RL, Ozenberger B, Roden DM, Williams MS, et al. Implementing genomic medicine in the clinic: The future is here. Genet Med 2013; 15(4): 258–267. doi: 10.1038/gim.2012.157

Kannry J, McCullagh L, Kushniruk A, Mann D, Edonyabo D, et al. A framework for usable and effective clinical decision support: Experience from the iCPR randomized clinical trial. EGEMS (Wash DC) 2015; 3(2): 1150. doi: 10.13063/2327-9214.1150

Kho AN, Cashy JP, Jackson KL, Pah AR, Goel S, et al. Design and implementation of a privacy preserving electronic health record linkage tool in Chicago. J Am Med Inform Assoc 2015; 22(5): 1072–1080. doi: 10.1093/jamia/ocv038

Kho AN, Hynes DM, Goel S, Solomonides AE, Price R, et al. CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network. J Am Med Inform Assoc 2014; 21(4): 607–611. doi: 10.1136/amiajnl-2014-002827

Lennerz JK, McLaughlin HM, Baron JM, Rasmussen D, Shin MS, et al. Health care infrastructure for financially sustainable clinical genomics. J Mol Diagn 2016; 18(5): 697–706. doi: 10.1016/j.jmoldx.2016.04.003

Manolio TA. Implementing genomics and pharmacogenomics in the clinic: The National Human Genome Research Institute’s genomic medicine portfolio. Atherosclerosis 2016; 253: 225–236. doi: 10.1016/j.atherosclerosis.2016.08.034

Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, et al. A conceptual model for translating omic data into clinical action. J Pathol Inform 2015; 6: 46. doi: 10.4103/2153-3539.163985

Weiss, A., Gill J, Goldberg J, Lagmay J, Spraker-Perlman H, et al. Advances in therapy for pediatric sarcomas. Curr Oncol Rep 2014; 16(8): 395. doi: 10.1007/s11912-014-0395-z

Uzilov AV, Ding W, Fink MY, Antipin Y, Brohl AS, et al. Development and clinical application of an integrative genomic approach to personalized cancer therapy. Genome Med 2016; 8(1): 62. doi: 10.1186/s13073-016-0313-0

Joseph SO, Wu J, Muggia FM. Targeted therapy: Its status and promise in selected solid tumors. Part II: Impact on selected tumor subsets, and areas of evolving integration. Oncology (Williston Park) 2012; 26(11): 1021–1030, 1035.

Palazzo A, Lacovelli R, Cortesi E. Past, present and future of targeted therapy in solid tumors. Curr Cancer Drug Targets 2010; 10(5): 433–461. doi: 10.2174/156800910791517145

Snozek CL, O’Kane DJ, Algeciras-Schimnich A. Pharmacogenetics of solid tumors: Directed therapy in breast, lung, and colorectal cancer. A paper from the 2008 William Beaumont Hospital Symposium on Molecular Pathology. J Mol Diagn 2009; 11(5): 381–389. doi: 10.2353/jmoldx.2009.090003

Loeb DM. Targeted therapies for sarcomas: The next generation of treatments [Internet]. The Liddy Shriver Sarcoma Initiative; 2007 [cited YYYY MM DD]. Available from:

Hood L. Systems biology and p4 medicine: Past, present, and future. Rambam Maimonides Med J 2013; 4(2): e0012. doi: 10.5041/RMMJ.10112

Sagner M, McNeil A, Puska P, Auffray C, Price ND. The P4 health spectrum—A predictive, preventive, personalized and participatory continuum for promoting healthspan. Prog Cardiovasc Dis 2016; 59(5): 506–521. doi: 10.1016/j.pcad.2016.08.002

Meany DL, Chan DW. Aberrant glycosylation associated with enzymes as cancer biomarkers. Clin Proteomics 2011; 8(1): 7. doi: 10.1186/1559-0275-8-7

Nakayama KI, Nakayama K. Ubiquitin ligases: cell-cycle control and cancer. Nat Rev Cancer 2006; 6(5): 369–381. doi: 10.1038/nrc1881

Naro C, Sette C. Phosphorylation-mediated regulation of alternative splicing in cancer. Int J Cell Biol 2013; 2013: 151839. doi: 10.1155/2013/151839

Papanikolaou NA, Papavassiliou AG. Protein complex, gene, and regulatory modules in cancer heterogeneity. Mol Med 2008; 14(9–10): 543–545. doi: 10.2119/2008-00083.Papanikolaou

Korolev KS, Xavier JB, Gore J. Turning ecology and evolution against cancer. Nat Rev Cancer 2014; 14(5): 371–380. doi: 10.1038/nrc3712

Pacheco JM, Santos FC, Dingli D. The ecology of cancer from an evolutionary game theory perspective. Interface Focus 2014; 4(4): 20140019. doi: 10.1098/rsfs.2014.0019

Greaves M. Evolutionary determinants of cancer. Cancer Discov 2015; 5(8): 806–820. doi: 10.1158/2159-8290.CD-15-0439

Fodor A. Utilizing “omics” tools to study the complex gut ecosystem. Adv Exp Med Biol 2014; 817: 25–38. doi: 10.1007/978-1-4939-0897-4_2

Schwabe RF, Jobin C. The microbiome and cancer. Nat Rev Cancer 2013; 13(11): 800–812. doi: 10.1038/nrc3610

Stacchiotti S, Astolfi A, Gronchi A, Fontana A, Pantaleo MA, et al. Evolution of dermatofibrosarcoma protuberans to DFSP-derived fibrosarcoma: An event marked by epithelial-mesenchymal transition-like process and 22q loss. Mol Cancer Res 2016; 14(9): 820–829. doi: 10.1158/1541-7786.MCR-16-0068

McGranahan N, Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 2015; 27(1): 15–26. doi: 10.1016/j.ccell.2014.12.001

Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 2013; 14(10): 1014–1022. doi: 10.1038/ni.2703

Whiteside TL. The tumor microenvironment and its role in promoting tumor growth. Oncogene 2008; 27(45): 5904–5912. doi: 10.1038/onc.2008.271

D’Souza-Schorey C, Clancy JW. Tumor-derived microvesicles: Shedding light on novel microenvironment modulators and prospective cancer biomarkers. Genes Dev 2012; 26(12): 1287–1299. doi: 10.1101/gad.192351.112

Landskron G, la Fuente MD, Thuwajit P, Thuwajit C, Hermoso MA. Chronic inflammation and cytokines in the tumor microenvironment. J Immunol Res 2014. 2014: 149185. doi: 10.1155/2014/149185

Quail DF, Taylor MJ, and Postovit LM. Microenvironmental regulation of cancer stem cell phenotypes. Curr Stem Cell Res Ther 2012; 7(3): 197–216. doi: 10.2174/157488812799859838



  • There are currently no refbacks.

Copyright (c) 2018 Ricardo J. Flores, Aaron J. Kelly, Manjula Nakka, Xiang Chen, Jiayi Sun, Tsz-Kwong Man

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.