Lung cancer heterogeneity plays an important role in the development of drug resistance. Comprehensive molecular characterizations of lung cancer can describe hereditary and somatic gene changes, mutation, and heterogeneity. We discuss heterogeneity specificity, characterization, and roles of PIK3CD, TP53, and KRAS, as well as target-driven therapies and strategies applied in clinical trials based on a proposed precise self-validation system. The system is a specifically selected strategy of treatment for patients with cancer gene mutations and heterogeneity based on gene sequencing, following validation of the strategies in the patient's own cancer cells or in patient-derived xenografts using their own cancer cells isolated during surgery or biopsies. These results will be more precise if the drugs used in the strategies are selected through protein structure–guided compound screening or a DNA-encoded chemical library before validation in the patient's own cancer cells. Thus, a deeper understanding of heterogeneity mechanisms and improved validation of the therapeutic strategy will result in more precise treatments for patients.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Dolsten M, Søgaard M. 1.  2012. Precision medicine: an approach to R&D for delivering superior medicines to patients. Clin. Transl. Med. 1:17Brings precision medicine into drug discovery and development and makes clear criteria of evaluation. [Google Scholar]
  2. Zhao B, Hemann MT, Lauffenburger DA. 2.  2014. Intratumor heterogeneity alters most effective drugs in designed combinations. PNAS 111:2910773–78 [Google Scholar]
  3. Wang X. 3.  2016. Gene mutation-based and specific therapies in precision medicine. J. Cell Mol. Med. 20:4577–80 [Google Scholar]
  4. Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL. 4.  et al. 2016. Cancer treatment and survivorship statistics, 2016.. CA Cancer J. Clin. 66:4271–89 [Google Scholar]
  5. Chen CS, He MY, Zhu YC, Shi L, Wang X. 5.  2015. Five critical elements to ensure the precision medicine. Cancer Metastasis Rev 34:2313–18 [Google Scholar]
  6. Wang DC, Wang X. 6.  2016. Systems heterogeneity: an integrative way to understand cancer heterogeneity. Semin. Cell. Dev. Biol. 64:1–4Described the concept of systems heterogeneity for the first time. [Google Scholar]
  7. Bailey-Wilson JE, Amos CI, Pinney SM, Petersen GM, de Andrade M. 7.  et al. 2004. A major lung cancer susceptibility locus maps to chromosome 6q23–25. Am. J. Hum. Genet. 75:3460–74 [Google Scholar]
  8. Landi MT, Chatterjee N, Yu K, Goldin LR, Goldstein AM. 8.  et al. 2009. A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am. J. Hum. Genet. 85:5679–91 [Google Scholar]
  9. Wu X, Yuan B, López E, Bai C, Wang X. 9.  2014. Gene polymorphisms and chronic obstructive pulmonary disease. J. Cell Mol. Med. 18:115–26 [Google Scholar]
  10. Wang X. 10.  2016. New biomarkers and therapeutics can be discovered during COPD-lung cancer transition. Cell Biol. Toxicol. 32:5359–61 [Google Scholar]
  11. Wang DC, Shi L, Zhu Z, Gao D, Zhang Y. 11.  2017. Genomic mechanisms of transformation from chronic obstructive pulmonary disease to lung cancer. Semin. Cancer Biol. 42:52–59 [Google Scholar]
  12. Yurgelun MB, Chenevix-Trench G, Lippman SM. 12.  2017. Translating germline cancer risk into precision prevention. Cell 168:4566–70 [Google Scholar]
  13. Gu J, Wang X. 13.  2017. Piercing the “armor” of lung cancer with genome medicine. Semin. Cancer Biol. 42:1–3 [Google Scholar]
  14. Fishbein L, Leshchiner I, Walter V, Danilova L, Robertson AG. 14.  et al. 2017. Comprehensive molecular characterization of pheochromocytoma and paraganglioma. Cancer Cell 31:2181–93 [Google Scholar]
  15. Yang X, Wu J, Lu J, Liu G, Di G. 15.  et al. 2015. Identification of a comprehensive spectrum of genetic factors for hereditary breast cancer in a Chinese population by next-generation sequencing. PLOS ONE 10:4e0125571 [Google Scholar]
  16. Yurgelun MB, Masciari S, Joshi VA, Mercado RC, Lindor NM. 16.  et al. 2015. Germline TP53 mutations in patients with early-onset colorectal cancer in the colon cancer family registry. JAMA Oncol 1:2214–21 [Google Scholar]
  17. Lu J, Wang W, Xu X, Li Y, Chen C, Wang X. 17.  2017. A global view of regulatory networks in lung cancer: an approach to understand homogeneity and heterogeneity. Semin. Cancer Biol. 42:31–38 [Google Scholar]
  18. Zhang Y, Wang DC, Shi L, Zhu B, Min Z, Jin J. 18.  2017. Genome analyses identify the genetic modification of lung cancer subtypes. Semin. Cancer Biol. 42:20–30 [Google Scholar]
  19. Wang J, Zhang Y, Wang HY, Dong N, Bao LN. 19.  et al. 2015. Global analyses of subtype- or stage-specific genes on chromosome 7 in patients with lung cancer. Cancer Metastasis Rev 34:2333–45 [Google Scholar]
  20. George J, Lim JS, Jang SJ, Cun Y, Ozretić L. 20.  2015. Comprehensive genomic profiles of small cell lung cancer. Nature 524:756347–53A full description of comprehensive molecular characterization in lung cancer with a new vision. [Google Scholar]
  21. Linehan WM, Spellman PT, Ricketts CJ, Creighton CJ, Fei SS. 21.  et al. 2016. Comprehensive molecular characterization of papillary renal cell carcinoma. N. Engl. J. Med. 374:2135–45 [Google Scholar]
  22. Wang X, Baumgartner C, Shields DS, Deng HW, Beckmann JS. 22.  2016. Translational Bioinformatics 11 Application of Clinical Bioinformatics Dordrecht, Neth.: Springer
  23. Wu D, Wang X. 23.  2015. Application of clinical bioinformatics in lung cancer-specific biomarkers. Cancer Metastasis Rev 34:2209–16 [Google Scholar]
  24. Wang DC, Wang X. 24.  2017. Tomorrow's genome medicine in lung cancer. Semin. Cancer Biol. 42:39–43 [Google Scholar]
  25. Wang X, Li K, Chen H, Wang D, Zhang Y, Bai C. 25.  2010. Does hepatocyte growth factor/c-Met signal play synergetic role in lung cancer?. J. Cell Mol. Med. 14:4833–39 [Google Scholar]
  26. Kwak EL, Ahronian LG, Siravegna G, Mussolin B, Godfrey JT. 26.  et al. 2015. Molecular heterogeneity and receptor co-amplification drive resistance to targeted therapy in MET-amplified esophagogastric cancer. Cancer Discov 5:121271–81 [Google Scholar]
  27. Tubbs A, Nussenzweig A. 27.  2017. Endogenous DNA damage as a source of genomic instability in cancer. Cell 168:4644–56Molecular mechanism of gene mutation and heterogeneity and the importance of genomic instability. [Google Scholar]
  28. de Bruin EC, McGranahan N, Mitter R, Salm M, Wedge DC. 28.  et al. 2014. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346:6206251–56 [Google Scholar]
  29. Ferguson LR, Chen H, Collins AR, Connell M, Damia G. 29.  et al. 2015. Genomic instability in human cancer: molecular insights and opportunities for therapeutic attack and prevention through diet and nutrition. Semin. Cancer Biol. 35:Suppl.S5–24 [Google Scholar]
  30. Nishida K, Arazoe T, Yachie N, Banno S, Kakimoto M. 30.  et al. 2016. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science 353:6503aaf8729 [Google Scholar]
  31. Fang H, Wang W. 31.  2016. Could CRISPR be the solution for gene editing's Gordian knot?. Cell Biol. Toxicol. 32:6465–67 [Google Scholar]
  32. Thomas PD, Kahn M. 32.  2016. Kat3 coactivators in somatic stem cells and cancer stem cells: biological roles, evolution, and pharmacologic manipulation. Cell Biol. Toxicol. 32:161–81 [Google Scholar]
  33. Komor AC, Kim YB, Packer MS, Zuris JA, Liu DR. 33.  2016. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533:7603420–24Molecular mechanisms and methodology of target gene editing. [Google Scholar]
  34. Wang X. 34.  2016. Cancer Moonshot 2020: a new march of clinical and translational medicine. Clin. Transl. Med. 5:11 [Google Scholar]
  35. Kalsi N, Gopalakrishnan C, Rajendran V, Purohit R. 35.  2016. Biophysical aspect of phosphatidylinositol 3-kinase and role of oncogenic mutants (E542K & E545K). J. Biomol. Struct. Dyn. 34:122711–21 [Google Scholar]
  36. Chen QY, Jiao DM, Zhu Y, Hu H, Wang J. 36.  et al. 2016. Identification of carcinogenic potential-associated molecular mechanisms in CD133+ A549 cells based on microRNA profiles. Tumour Biol 37:1521–30 [Google Scholar]
  37. Wang W, Lv J, Wang L, Wang X, Ye L. 37.  2016. The impact of heterogeneity in phosphoinositide 3-kinase pathway in human cancer and possible therapeutic treatments. Semin. Cell. Dev. Biol. 64:116–24 [Google Scholar]
  38. Sutherland KD, Berns A. 38.  2010. Cell of origin of lung cancer. Mol. Oncol. 4:5397–403 [Google Scholar]
  39. Han X, Li F, Fang Z, Gao Y, Li F. 39.  et al. 2014. Transdifferentiation of lung adenocarcinoma in mice with Lkb1 deficiency to squamous cell carcinoma. Nat. Commun. 5:3261 [Google Scholar]
  40. Scheffler M, Bos M, Gardizi M, König K, Michels S. 40.  et al. 2015. PIK3CA mutations in non-small cell lung cancer (NSCLC): genetic heterogeneity, prognostic impact and incidence of prior malignancies. Oncotarget 6:21315–26 [Google Scholar]
  41. Burke JE, Williams RL. 41.  2015. Synergy in activating class I PI3Ks. Trends Biochem. Sci. 40:288–100 [Google Scholar]
  42. Kim HR, Cho BC, Shim HS, Lim SM, Kim SK. 42.  et al. 2014. Prediction for response duration to epidermal growth factor receptor-tyrosine kinase inhibitors in EGFR mutated never smoker lung adenocarcinoma. Lung Cancer 83:3374–82 [Google Scholar]
  43. Quéré G, Descourt R, Robinet G, Autret S, Raguenes O. 43.  et al. 2016. Mutational status of synchronous and metachronous tumor samples in patients with metastatic non-small-cell lung cancer. BMC Cancer 16:210 [Google Scholar]
  44. Chan LWC, Wang F, Meng F, Wang L, Wong SCC. 44.  et al. 2017. MiR-30 family potentially targeting PI3K-SIAH2 predicted interaction network represents a novel putative theranostic panel in non-small cell lung cancer. Front Genet 8:8 [Google Scholar]
  45. Imai Y, Yamagishi H, Ono Y, Ueda Y. 45.  2011. Versatile inhibitory effects of the flavonoid-derived PI3K/Akt inhibitor, LY294002, on ATP-binding cassette transporters that characterize stem cells. Clin. Transl. Med. 1:124 [Google Scholar]
  46. Herbel C, Patsoukis N, Bardhan K, Seth P, Weaver JD, Boussiotis VA. 46.  2016. Clinical significance of T cell metabolic reprogramming in cancer. Clin. Transl. Med. 5:129 [Google Scholar]
  47. Castro M, Grau L, Puerta P, Gimenez L, Venditti J. 47.  et al. 2010. Multiplexed methylation profiles of tumor suppressor genes and clinical outcome in lung cancer. J. Transl. Med. 8:86 [Google Scholar]
  48. Xu J, Zhou W, Yang F, Chen G, Li H. 48.  et al. 2017. The β-TrCP-FBXW2-SKP2 axis regulates lung cancer cell growth with FBXW2 acting as a tumour suppressor. Nat. Commun. 8:14002 [Google Scholar]
  49. Krimmel JD, Schmitt MW, Harrell MI, Agnew KJ, Kennedy SR. 49.  et al. 2016. Ultra-deep sequencing detects ovarian cancer cells in peritoneal fluid and reveals somatic TP53 mutations in noncancerous tissues. PNAS 113:216005–10 [Google Scholar]
  50. Zanjirband M, Edmondson RJ, Lunec J. 50.  2016. Pre-clinical efficacy and synergistic potential of the MDM2-p53 antagonists, Nutlin-3 and RG7388, as single agents and in combined treatment with cisplatin in ovarian cancer. Oncotarget 7:2640115–34 [Google Scholar]
  51. Hou J, Zhang Y, Zhu Z. 51.  2016. Gene heterogeneity in metastasis of colorectal cancer to the lung. Semin. Cell Dev. Biol. 64:58–64 [Google Scholar]
  52. Kim JY, Stewart PA, Borne AL, Fang B, Welsh EA. 52.  et al. 2016. Activity-based proteomics reveals heterogeneous kinome and ATP-binding proteome responses to MEK inhibition in KRAS mutant lung cancer. Proteomes 4:216 [Google Scholar]
  53. Didenko VV, Wang XD, Yang LQ, Hornsby PJ. 53.  1996. Expression of p21WAF1/CIP1/SDI1 and p53 in apoptotic cells in the adrenal cortex and induction by ischemia/reperfusion injury. J. Clin. Investig. 97:1723–31 [Google Scholar]
  54. Wang XD, Sun ZW, Andersson R. 54.  1999. Autohepatocyte transplantation in a degradable collagen device in the bursa omentalis. Transplant. Proc. 31:2138–42 [Google Scholar]
  55. Visonneau S, Cesano A, Torosian MH, Miller EJ, Santoli D. 55.  1998. Growth characteristics and metastatic properties of human breast cancer xenografts in immunodeficient mice. Am. J. Pathol. 152:51299–311 [Google Scholar]
  56. Ambrogio C, Nadal E, Villanueva A, Gómez-López G, Cash TP. 56.  et al. 2016. KRAS-driven lung adenocarcinoma: combined DDR1/Notch inhibition as an effective therapy. ESMO Open 1:5e000076 [Google Scholar]
  57. Gardner EE, Lok BH, Schneeberger VE, Desmeules P, Miles LA. 57.  et al. 2017. Chemosensitive relapse in small cell lung cancer proceeds through an EZH2-SLFN11 axis. Cancer Cell 31:2286–99Induction of chemosensitive and nonchemosensitive models to evaluate target drugs. [Google Scholar]
  58. Schütte M, Risch T, Abdavi-Azar N, Boehnke K, Schumacher D. 58.  et al. 2017. Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors. Nat. Commun. 8:14262 [Google Scholar]
  59. Gu Q, Zhang B, Sun H, Xu Q, Tan Y. 59.  et al. 2015. Genomic characterization of a large panel of patient-derived hepatocellular carcinoma xenograft tumor models for preclinical development. Oncotarget 6:2420160–76 [Google Scholar]
  60. Lee DH. 60.  2017. Treatments for EGFR-mutant non-small cell lung cancer (NSCLC): the road to a success, paved with failures. Pharmacol. Ther. 174:1–21 [Google Scholar]
  61. Dong N, Shi L, Wang DC, Chen C, Wang X. 61.  2016. Role of epigenetics in lung cancer heterogeneity and clinical implication. Semin. Cell Dev. Biol. 64:18–25 [Google Scholar]
  62. Neri F, Rapelli S, Krepelova A, Incarnato D, Parlato C. 62.  et al. 2017. Intragenic DNA methylation prevents spurious transcription initiation. Nature 543:764372–77 [Google Scholar]
  63. Wang L, Zhu B, Zhang M, Wang X. 63.  2016. Roles of immune microenvironment heterogeneity in therapy-associated biomarkers in lung cancer. Semin. Cell Dev. Biol. 64:90–97 [Google Scholar]
  64. Wu D, Zhuo L, Wang X. 64.  2016. Metabolic reprogramming of carcinoma-associated fibroblasts and its impact on metabolic heterogeneity of tumors. Semin. Cell Dev. Biol. 64:125–31 [Google Scholar]
  65. Niu B, Scott AD, Sengupta S, Bailey MH, Batra P. 65.  et al. 2016. Protein-structure-guided discovery of functional mutations across 19 cancer types. Nat. Genet. 48:8827–37Approach to screen target drugs through protein-structure-guided discovery based on gene function and mutation. [Google Scholar]
  66. Goodnow RA Jr., Dumelin CE, Keefe AD. 66.  2017. DNA-encoded chemistry: enabling the deeper sampling of chemical space. Nat. Rev. Drug Discov. 16:2131–47Importance of DNA-encoded chemistry in drug discovery and development against precise targets. [Google Scholar]
  67. Franzini RM, Neri D, Scheuermann J. 67.  2014. DNA-encoded chemical libraries: advancing beyond conventional small-molecule libraries. Acc. Chem. Res. 47:41247–55 [Google Scholar]
  68. Wu D, Wang DC, Cheng Y, Qian M, Zhang M. 68.  et al. 2017. Roles of tumor heterogeneity in the development of drug resistance: a call for precision therapy. Semin. Cancer Biol. 42:13–19 [Google Scholar]
  69. Shi L, Wang X. 69.  2016. Role of osteopontin in lung cancer evolution and heterogeneity. Semin. Cell. Dev. Biol. 64:40–47 [Google Scholar]
  70. Xu M, Wang DC, Wang X, Zhang Y. 70.  2016. Correlation between mucin biology and tumor heterogeneity in lung cancer. Semin. Cell. Dev. Biol. 64:73–78 [Google Scholar]
  71. Wang X, Ward PA. 71.  2012. Opportunities and challenges of disease biomarkers: a new section in the Journal of Translational Medicine. J. Transl. Med. 10:220 [Google Scholar]
  72. Niu F, Wang DC, Lu J, Wu W, Wang X. 72.  2016. Potentials of single-cell biology in identification and validation of disease biomarkers. J. Cell Mol. Med. 20:91789–95 [Google Scholar]
  73. Wang J, Song Y. 73.  2017. Single cell sequencing: a distinct new field. Clin. Transl. Med. 6:110 [Google Scholar]
  74. Qian M, Wang DC, Chen H, Cheng Y. 74.  2016. Detection of single cell heterogeneity in cancer. Semin. Cell Dev. Biol. 64:143–49 [Google Scholar]
  75. Prasetyanti PR, Medema JP. 75.  2017. Intra-tumor heterogeneity from a cancer stem cell perspective. Mol. Cancer 16:141 [Google Scholar]
  76. Zhang K. 76.  2017. Stratifying tissue heterogeneity with scalable single-cell assays. Nat. Methods 14:3238–39 [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error