Cancer develops due to the acquisition of somatic mutations – those are the mutations you acquire throughout your life, they are not inherited nor passed to the offspring. These are the opposite of germline mutations, which are inherited from ancestors or gained very early during embryogenesis, in the uterus. For many years it has been clear that cancer is a genetic disease, or more specifically that cancer is a disease of our genome; however, it took many years to conclude that an individual patient’s prognosis and treatment strategy cannot be based only on a single lab test due to the complexity of the disease. Even pathomorphological assay might not be enough, especially if we want to administer targeted therapy. Biopsy and detailed genetic analysis are necessary. 

Cancer development risk increases with age. However, some cancer types differ in their prognosis and symptoms between adults and children. For example, childhood acute myeloid leukaemia, AML, is more often connected with complicated infections and increased mortality. Similarly, breast cancer at an early age is usually more aggressive and difficult to treat. 

Cancer heterogeneity revealed by sequencing

Recent studies shed new light on the molecular landscape of cancer as a highly heterogeneous disease, composed of many clones with distinct mutational patterns and mutational signatures, therefore presenting differential susceptibilities to treatment. This phenomenon explains why some cancers progress rapidly after chemotherapy, leading to patients’ relapse, while other cancers respond well and the effects of treatment are long-lasting. 

Cancer initiation and further progression occur due to the positive selection of some “driver” mutations. Such mutations inactivate tumour suppressor genes or activate oncogenes. Average cancer contains 2-8 drivers and the total number of drivers seem to be definite since all of those genes affect such pathways as apoptosis, proliferation, chromatin regulation and genome stability. Other mutations, which were not positively selected during cancer evolution, are called “passengers”.  

To facilitate the hunt for drivers, new treatment strategies and prevention methods, as the foundation of personalised medicine, a catalogue of genetic changes in cancer was created – The Cancer Genome Atlas. Information from this project is used in online genomic databases, e.g. Cosmic. Next Generation Sequencing techniques have revolutionized cancer research, enabling fast and precise detection of tumour-specific mutations, as well as cancer biomarkers. In most cases, such mutations are located in the coding sequence of a gene, leading to aberrations in that particular signalling pathway. Despite this,  cancer biomarkers are often much more complicated, consisting of entire chromosomal fragments, small structural variants, or even combinations thereof.

Cancer changes over time

Globally-expressed genes have more important functions, such as housekeeping genes. Since most positively-selected cancer mutations are located within globally-expressed genes, the development of therapeutic agents targeting them without causing harm to healthy cells could be even more challenging. The significance of mutation order remains unclear. The initial mutation can possibly change the cellular composition, facilitating the next mutation to occur, creating a more “friendly” environment for another change, genetic or epigenetic. One of the mutations will provide a growth advantage and pass positive selection, leading to tumour growth. 

In fact, tyrosine kinase inhibitors, such as imatinib have revolutionized cancer treatment, starting from leukaemia. However, dynamically evolving cancer can respond to selective pressure and gain new mutations, even during clinical trials and the treatment process itself. Intratumoural and Intertumoural heterogeneity lead to the evolution of resistance. New approaches to cancer evolution appeared with the advent of NGS methods. Cancer is no longer taken as a whole, but rather as a sum of subclones, related to each other, arising from a single ancestral cell that collected different somatic mutations over time. Selection acts on individual dividing cells to confer a survival advantage; it operates on cell’s phenotypes, which are unstable and prone to modifications by both internal and external factors. Some of these changes are very stable, such as oncogenic mutations. 

Personalised cancer therapy should take into account real-time tracking of a patient’s genomic landscape or at least any changes (CT scans), also microenvironmental changes, such as different cytokine levels, for example, IL-2, IL-8, IL-15. This inevitably increases the price of already expensive personalised therapy but brings confidence that the therapy really works.

Every cancer cell might be different

In very rare cases, mutation can improve cell functioning and give a survival advantage to the subclone population. A tumour is in fact highly heterogeneous. Methods of cancer stratification are usually based on cancer panel tests, which reflect mostly those mutations that are present in the majority of cancer cells, neglecting minor subclones. These subclones could be equally clinically relevant, providing resistance to the treatment used. Most modern chemotherapeutics are capable of eradicating the majority of cancer cells; despite this, failing to remove even a small fraction of cancer subclones can result in cancer recurrence. This is one of the reasons why we often administer a combination of drugs rather than a single agent, trying to maximise the positive treatment outcome. Combining more than one drug and treatment advancement is often crucial for final success. Fast identification and prognostic evaluation of all clones present in particular cancer might be necessary for proper molecular diagnosis and choosing the best treatment scenario for an auspicious outcome. According to the bottleneck effect, after treatment, such resistant clones will expand soon after the treatment and become dominant, causing the disease’s relapse. In such cases, organoid structures can be established in vitro using a patient’s cancer cells of individual tumour clonal lineages. Every subclone should be carefully sequenced and treated according to mutations present – firstly in the culture, but after successful laboratory trials, the most effective treatment could be given to the patient. It is also relevant to establish a suitable treatment plan, including whether to treat cancer clones simultaneously, or one by one. If the cancer is polyclonal and in an advanced stage, combination therapies could be implemented.

Cancer recurrence theories

Two theories have been formulated to explain the emergence of intratumoural heterogeneity: the model of cancer stem cells (CSC) and the model of clonal evolution. Historically, both models were presented as separate and exclusive processes. Some cancers were believed to follow CSCs model, whereas other types of cancer – as following the clonal evolution model. Now it becomes clear that in most cancer types both models are relevant, contributing to cancer heterogeneity and complexity. 

The first model states that only a small subset of CSCs exist and possess the capacity to sustain cancer development through self-renewal, proliferation, differentiation and all characteristics typical to normal stem cells. It is believed that thanks to the differentiation process, those daughter cells can also form cancer heterogeneity and hierarchy. In contrast, the clonal evolution model establishes competition between genetically diverse cancer subclones, which might be similar to the “survival of the fittest” rule in the theory of evolution. Every cell has equal potential for growth and development, depending on factors such as microenvironment, external influences, or simply by chance. Cancer cells within the same clone will share similar genotypes, but they can behave differently depending on the environment (context), for example, the presence of particular chemicals or oxygen. Such exposure leads to the waxing and waning of subclonal populations of cancer cells within the same tumour.

Purifying selection could be typical of germline mutations, likely due to the fact that organisms are already well-adapted. In contrast, cancer mutations are affected mostly by positive selection, which reflects the selection of those driver mutations, which give adaptive properties to the cancer cells. Recurrent cancer might have some mutations present at diagnosis, but not necessarily – some cells could have escaped from therapy and gained new mutations, allowing them to survive and progress. 

Personalised medicine in action

Personalised medicine uses genomic profiles to identify a patient’s tumour characteristics, based on molecular cancer signatures. The aim is to target the most dangerous mutation and all of the subclones, preventing further progression or recurrence. Modern genomics, with the aid of AI-based algorithms, may also provide information about the best treatment for each tumour, ranking cancer therapies from the one with the highest probability of being effective, to the one that possibly won’t work at all. This is a major breakthrough, allowing clinicians to pick the right treatment for the right patient without wasting their precious time. This approach is also cost-effective and prevents unnecessary suffering – since every treatment is accompanied by side effects, patients do not undergo ineffective treatment regimens at all. The ultimate goal for all clinical practices should be the complete eradication of the disease, with all its subclonal populations, to prevent self-restoring and relapse.

Bibliography

  1. Albitar, M. and Donahue, A. (2010) ‘Molecular pathology of leukemia’, in O’Brien, S., Kantarjian, H.M., and Vose, J.M. (eds.) Management of hematologic malignancies. Cambridge: Cambridge University Press.
  2. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Aparicio, S.A.J.R., Behjati, S., Biankin, A.V., Bignell, G.R., Bolli, N., Borg, A., Børresen-Dale, A.-L., Boyault, S., Burkhardt, B., Butler, A.P., Caldas, C., Davies, H.R., Desmedt, C., Eils, R., Eyfjörd, J.E., Foekens, J.A., Greaves, M., Hosoda, F., Hutter, B., Ilicic, T., Imbeaud, S., Imielinsk, M., Jäger, N., Jones, D.T.W., Knappskog, S., Kool, M., Lakhani, S.R., López-Otín, C., Martin, S., Munshi, N.C., Nakamura, H., Northcott, P.A., Pajic, M., Papaemmanuil, E., Paradiso, A., Pearson, J.V., Puente, X.S., Raine, K., Ramakrishna, M., Richardson, A.L., Richter, J., Rosenstiel, P., Schlesner, M., Schumacher, T.N., Span, P.N., Teague, J.W., Totoki, Y., Tutt, A.N.J., Valdés-Mas, R., van Buuren, M.M., van ’t Veer, L., Vincent-Salomon, A., Waddell, N., Yates, L.R., Zucman-Rossi, J., Andrew Futreal, P., McDermott, U., Lichter, P., Meyerson, M., Grimmond, S.M., Siebert, R., Campo, E., Shibata, T., Pfister, S.M., Campbell, P.J. and Stratton, M.R. (2013) ‘Signatures of mutational processes in human cancer’, Nature, 500(7463), pp. 415–421. doi: 10.1038/nature12477.
  3. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Campbell, P.J. and Stratton, M.R. (2013) ‘Deciphering signatures of mutational processes operative in human cancer’, Cell Reports, 3(1), pp. 246–259. doi: 10.1016/j.celrep.2012.12.008.
  4. Aparicio, S. and Caldas, C. (2013) ‘The implications of Clonal genome evolution for cancer medicine’, New England Journal of Medicine, 368(9), pp. 842–851. doi: 10.1056/nejmra1204892.
  5. Aparicio, S. and Caldas, C. (2013) ‘The implications of Clonal genome evolution for cancer medicine’, New England Journal of Medicine, 368(9), pp. 842–851. doi: 10.1056/nejmra1204892.
  6. Apostoli, A.J. and Ailles, L. (2015) ‘Clonal evolution and tumor-initiating cells: New dimensions in cancer patient treatment’, Critical Reviews in Clinical Laboratory Sciences, 53(1), pp. 40–51. doi: 10.3109/10408363.2015.1083944.
  7. Biernaux, C., Loos, M., Sels, A., Huez, G. and Stryckmans, P. (1995) ‘Detection of Major bcr-abl Gene Expression at a Very Low Level in Blood Cells of Some Healthy Indywidualny’, Blood, 86(8), pp. 3118–3122.
  8. Bochennek, K., Hassler, A., Perner, C., Gilfert, J., Schöning, S., Klingebiel, T., Reinhardt, D., Creutzig, U. and Lehrnbecher, T. (2016) ‘Infectious complications in children with acute myeloid leukemia: Decreased mortality in multicenter trial AML-BFM 2004’, Blood Cancer Journal, 6(1), p. e382. doi: 10.1038/bcj.2015.110.
  9. Bower, H., Andersson, T.M.-L., Björkholm, M., Dickman, P.W., Lambert, P.C. and Derolf, Å.R. (2016) ‘Continued improvement in survival of acute myeloid leukemia patients: An application of the loss in expectation of life’, Blood Cancer Journal, 6(2), p. e390. doi: 10.1038/bcj.2016.3.
  10. Burgess, D.J. (2014) ‘Evolution: Cancer drivers everywhere?’, Nature Reviews Genetics, 15(5), pp. 289–289. doi: 10.1038/nrg3718.
  11. Conte, N., Varela, I., Grove, C., Manes, N., Yusa, K., Moreno, T., Segonds-Pichon, A., Bench, A., Gudgin, E., Herman, B., Bolli, N., Ellis, P., Haddad, D., Costeas, P., Rad, R., Scott, M., Huntly, B., Bradley, A. and Vassiliou, G.S. (2013) ‘Detailed molecular characterisation of acute myeloid leukaemia with a normal karyotype using targeted DNA capture’, Leukemia, 27(9), pp. 1820–1825. doi: 10.1038/leu.2013.117.
  12. Dimond, P.F. (2014) ‘Translating Omics into cancer biology and medicine’, Genetic Engineering & Biotechnology News, 34(10), pp. 23, 25. doi: 10.1089/gen.34.10.12.
  13. Ding, K., Wu, S., Ying, W., Pan, Q., Li, X., Zhao, D., Zhao, Q., Zhu, Y., Ren, H. and Qian, X. (2015) ‘Leveraging a Multi-Omics strategy for Prioritizing personalized candidate mutation-driver genes: A proof-of-concept study’, Scientific Reports, 5, p. 17564. doi: 10.1038/srep17564.
  14. Forsberg, L.A., Rasi, C., Razzaghian, H.R., Pakalapati, G., Waite, L., Thilbeault, K.S., Ronowicz, A., Wineinger, N.E., Tiwari, H.K., Boomsma, D., Westerman, M.P., Harris, J.R., Lyle, R., Essand, M., Eriksson, F., Assimes, T.L., Iribarren, C., Strachan, E., O’Hanlon, T.P., Rider, L.G., Miller, F.W., Giedraitis, V., Lannfelt, L., Ingelsson, M., Piotrowski, A., Pedersen, N.L., Absher, D. and Dumanski, J.P. (2012) ‘Age-related somatic structural changes in the nuclear genome of human blood cells’, The American Journal of Human Genetics, 90(2), pp. 217–228. doi: 10.1016/j.ajhg.2011.12.009.
  15. Gatenby, R.A., Cunningham, J.J. and Brown, J.S. (2014) ‘Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations’, Nature Communications, 5, p. 5499. doi: 10.1038/ncomms6499.
  16. Genovese, G., Kähler, A.K., Handsaker, R.E., Lindberg, J., Rose, S.A., Bakhoum, S.F., Chambert, K., Mick, E., Neale, B.M., Fromer, M., Purcell, S.M., Svantesson, O., Landén, M., Höglund, M., Lehmann, S., Gabriel, S.B., Moran, J.L., Lander, E.S., Sullivan, P.F., Sklar, P., Grönberg, H., Hultman, C.M. and McCarroll, S.A. (2014) ‘Clonal Hematopoiesis and blood-cancer risk inferred from blood DNA sequence’, New England Journal of Medicine, 371(26), pp. 2477–2487. doi: 10.1056/nejmoa1409405.
  17. Green, E.D., Guyer, M.S., Manolio, T.A. and Peterson, J.L. (2011) ‘Charting a course for genomic medicine from base pairs to bedside’, Nature, 470(7333), pp. 204–213. doi: 10.1038/nature09764.
  18. Grimwade, D., Ivey, A. and Huntly, B.J.P. (2015) ‘Molecular landscape of acute myeloid leukemia in younger adults and its clinical relevance’, Blood, 127(1), pp. 29–41. doi: 10.1182/blood-2015-07-604496.
  19. Grove, C.S. and Vassiliou, G.S. (2014) ‘Acute myeloid leukaemia: A paradigm for the clonal evolution of cancer?’, Disease Models & Mechanisms, 7(8), pp. 941–951. doi: 10.1242/dmm.015974.
  20. Huang, J.C., Basu, S.K., Zhao, X., Chien, S., Fang, M., Oehler, V.G., Appelbaum, F.R. and Becker, P.S. (2015) ‘Mesenchymal stromal cells derived from acute myeloid leukemia bone marrow exhibit aberrant cytogenetics and cytokine elaboration’, Blood Cancer Journal, 5(4), p. e302. doi: 10.1038/bcj.2015.17.
  21. Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J.F., Wyczalkowski, M.A., Leiserson, M.D.M., Miller, C.A., Welch, J.S., Walter, M.J., Wendl, M.C., Ley, T.J., Wilson, R.K., Raphael, B.J. and Ding, L. (2013) ‘Mutational landscape and significance across 12 major cancer types’, Nature, 502(7471), pp. 333–339. doi: 10.1038/nature12634.
  22. Kasi, P.M., Litzow, M.R., Patnaik, M.M., Hashmi, S.K. and Gangat, N. (2016) ‘Clonal evolution of AML on novel FMS-like tyrosine kinase-3 (FLT3) inhibitor therapy with evolving actionable targets’, Leukemia Research Reports, 5, pp. 7–10. doi: 10.1016/j.lrr.2016.01.002.
  23. Kosztolányi, G. (2014) ‘It is time to take timing seriously in clinical genetics’, European Journal of Human Genetics, 23(11), pp. 1435–1437. doi: 10.1038/ejhg.2014.271.
  24. Kruspig, B., Zhivotovsky, B. and Gogvadze, V. (2014) ‘Mitochondrial substrates in cancer: Drivers or passengers?’, Mitochondrion, 19, pp. 8–19. doi: 10.1016/j.mito.2014.08.007.
  25. Kuett, A., Rieger, C., Perathoner, D., Herold, T., Wagner, M., Sironi, S., Sotlar, K., Horny, H.-P., Deniffel, C., Drolle, H. and Fiegl, M. (2015) ‘IL-8 as mediator in the microenvironment-leukaemia network in acute myeloid leukaemia’, Scientific Reports, 5, p. 18411. doi: 10.1038/srep18411.
  26. Landau, D.A., Carter, S.L., Stojanov, P., McKenna, A., Stevenson, K., Lawrence, M.S., Sougnez, C., Stewart, C., Sivachenko, A., Wang, L., Wan, Y., Zhang, W., Shukla, S.A., Vartanov, A., Fernandes, S.M., Saksena, G., Cibulskis, K., Tesar, B., Gabriel, S., Hacohen, N., Meyerson, M., Lander, E.S., Neuberg, D., Brown, J.R., Getz, G. and Wu, C.J. (2013) ‘Evolution and impact of Subclonal mutations in chronic lymphocytic leukemia’, Cell, 152(4), pp. 714–726. doi: 10.1016/j.cell.2013.01.019.
  27. Lang, F., Wojcik, B. and Rieger, M.A. (2015) ‘Stem cell hierarchy and Clonal evolution in acute Lymphoblastic leukemia’, Stem Cells International, 2015, pp. 1–13. doi: 10.1155/2015/137164.
  28. Lawrence, M.S., Stojanov, P., Mermel, C.H., Robinson, J.T., Garraway, L.A., Golub, T.R., Meyerson, M., Gabriel, S.B., Lander, E.S. and Getz, G. (2014) ‘Discovery and saturation analysis of cancer genes across 21 tumour types’, Nature, 505(7484), pp. 495–501. doi: 10.1038/nature12912.
  29. Lindsley, R.C., Mar, B.G., Mazzola, E., Grauman, P.V., Shareef, S., Allen, S.L., Pigneux, A., Wetzler, M., Stuart, R.K., Erba, H.P., Damon, L.E., Powell, B.L., Lindeman, N., Steensma, D.P., Wadleigh, M., DeAngelo, D.J., Neuberg, D., Stone, R.M. and Ebert, B.L. (2014) ‘Acute myeloid leukemia ontogeny is defined by distinct somatic mutations’, Blood, 125(9), pp. 1367–1376. doi: 10.1182/blood-2014-11-610543.
  30. Makohon-Moore, A.P., Zhang, M., Reiter, J.G., Bozic, I., Wong, F., Jiao, Y., Chatterjee, K., Nowak, M., Papadopoulos, N., Vogelstein, B., Kinzler, K.W. and Iacobuzio-Donahue, C.A. (2015) ‘Abstract 4137: Clonal evolution defines the natural history of metastatic pancreatic cancer’, Cancer Research, 75(15 Supplement), pp. 4137–4137. doi: 10.1158/1538-7445.am2015-4137.
  31. McKerrell, T., Park, N., Moreno, T., Grove, C.S., Ponstingl, H., Stephens, J., Crawley, C., Craig, J., Scott, M.A., Hodkinson, C., Baxter, J., Rad, R., Forsyth, D.R., Quail, M.A., Zeggini, E., Ouwehand, W., Varela, I. and Vassiliou, G.S. (2015) ‘Leukemia-associated somatic mutations drive distinct patterns of age-related Clonal Hemopoiesis’, Cell Reports, 10(8), pp. 1239–1245. doi: 10.1016/j.celrep.2015.02.005.
  32. McKerrell, T. and Vassiliou, G.S. (2015) ‘Aging as a driver of leukemogenesis’, Science Translational Medicine, 7(306), pp. 306fs38–306fs38. doi: 10.1126/scitranslmed.aac4428.
  33. Mikhaylenko, D.., Efremov, G.., Sivkov, A.V. and Zaletaev, D.V. (2016) ‘Hormone resistance and neuroendocrine differentiation due to the accumulation of the genetic alterations during clonal evolution of prostate cnacer’, Molecular Biology, 50(1), pp. 34–43.
  34. Murtaza, M., Dawson, S.-J., Pogrebniak, K., Rueda, O.M., Provenzano, E., Grant, J., Chin, S.-F., Tsui, D.W.Y., Marass, F., Gale, D., Ali, H.R., Shah, P., Contente-Cuomo, T., Farahani, H., Shumansky, K., Kingsbury, Z., Humphray, S., Bentley, D., Shah, S.P., Wallis, M., Rosenfeld, N. and Caldas, C. (2015) ‘Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer’, Nature Communications, 6, p. 8760. doi: 10.1038/ncomms9760.
  35. Ortmann, C.A., Kent, D.G., Nangalia, J., Silber, Y., Wedge, D.C., Grinfeld, J., Baxter, E.J., Massie, C.E., Papaemmanuil, E., Menon, S., Godfrey, A.L., Dimitropoulou, D., Guglielmelli, P., Bellosillo, B., Besses, C., Döhner, K., Harrison, C.N., Vassiliou, G.S., Vannucchi, A., Campbell, P.J. and Green, A.R. (2015) ‘Effect of mutation order on Myeloproliferative Neoplasms’, New England Journal of Medicine, 372(7), pp. 601–612. doi: 10.1056/nejmoa1412098.
  36. Paugh, S.W., Bonten, E.J., Savic, D., Ramsey, L.B., Thierfelder, W.E., Gurung, P., Malireddi, R.K.S., Actis, M., Mayasundari, A., Min, J., Coss, D.R., Laudermilk, L.T., Panetta, J.C., McCorkle, J.R., Fan, Y., Crews, K.R., Stocco, G., Wilkinson, M.R., Ferreira, A.M., Cheng, C., Yang, W., Karol, S.E., Fernandez, C.A., Diouf, B., Smith, C., Hicks, J.K., Zanut, A., Giordanengo, A., Crona, D., Bianchi, J.J., Holmfeldt, L., Mullighan, C.G., Boer, M.L. den, Pieters, R., Jeha, S., Dunwell, T.L., Latif, F., Bhojwani, D., Carroll, W.L., Pui, C.-H., Myers, R.M., Guy, R.K., Kanneganti, T.-D., Relling, M.V. and Evans, W.E. (2015) ‘NALP3 inflammasome upregulation and CASP1 cleavage of the glucocorticoid receptor cause glucocorticoid resistance in leukemia cells’, Nature Genetics, 47(6), pp. 607–614. doi: 10.1038/ng.3283.
  37. Paulsson, K., Lilljebjörn, H., Biloglav, A., Olsson, L., Rissler, M., Castor, A., Barbany, G., Fogelstrand, L., Nordgren, A., Sjögren, H., Fioretos, T. and Johansson, B. (2015) ‘The genomic landscape of high hyperdiploid childhood acute lymphoblastic leukemia’, Nature Genetics, 47(6), pp. 672–676. doi: 10.1038/ng.3301.
  38. Raab, M.S., Lehners, N., Xu, J., Ho, A.D., Schirmacher, P., Goldschmidt, H. and Andrulis, M. (2016) ‘Spatially divergent clonal evolution in multiple myeloma: Overcoming resistance to BRAF inhibition’, Blood, . doi: 10.1182/blood-2015-12-686782.
  39. Renneville, A., Roumier, C., Biggio, V., Nibourel, O., Boissel, N., Fenaux, P. and Preudhomme, C. (2008) ‘Cooperating gene mutations in acute myeloid leukemia: A review of the literature’, Leukemia, 22(5), pp. 915–931. doi: 10.1038/leu.2008.19.
  40. Riva, L., Luzi, L. and Pelicci, P.G. (2012) ‘Genomics of acute myeloid leukemia: The next generation’, Frontiers in Oncology, 2. doi: 10.3389/fonc.2012.00040.
  41. Sakoparnig, T., Fried, P. and Beerenwinkel, N. (2015) ‘Identification of constrained cancer driver genes based on mutation timing’, PLoS Computational Biology, 11(1), p. e1004027. doi: 10.1371/journal.pcbi.1004027.
  42. Sakurai, M., Kasahara, H., Yoshida, K., Yoshimi, A., Kunimoto, H., Watanabe, N., Shiraishi, Y., Chiba, K., Tanaka, H., Harada, Y., Harada, H., Kawakita, T., Kurokawa, M., Miyano, S., Takahashi, S., Ogawa, S., Okamoto, S. and Nakajima, H. (2016) ‘Genetic basis of myeloid transformation in familial platelet disorder/acute myeloid leukemia patients with haploinsufficient RUNX1 allele’, Blood Cancer Journal, 6(2), p. e392. doi: 10.1038/bcj.2015.81.
  43. Savage, N. (2015) ‘Proteomics: High-protein research’, Nature, 527(7576), pp. S6–S7. doi: 10.1038/527s6a.
  44. Sawan, C., Vaissière, T., Murr, R. and Herceg, Z. (2008) ‘Epigenetic drivers and genetic passengers on the road to cancer’, Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 642(1-2), pp. 1–13. doi: 10.1016/j.mrfmmm.2008.03.002.
  45. Shlush, L.I., Zandi, S., Mitchell, A., Chen, W.C., Brandwein, J.M., Gupta, V., Kennedy, J.A., Schimmer, A.D., Schuh, A.C., Yee, K.W., McLeod, J.L., Doedens, M., Medeiros, J.J.F., Marke, R., Kim, H.J., Lee, K., McPherson, J.D., Hudson, T.J., Pan-Leukemia Gene Panel Consortium, T.H., Brown, A.M.K., Trinh, Q.M., Stein, L.D., Minden, M.D., Wang, J.C.Y. and Dick, J.E. (2014) ‘Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia’, Nature, 506(7488), pp. 328–333. doi: 10.1038/nature13038.
  46. Stölzel, F., Mohr, B., Kramer, M., Oelschlägel, U., Bochtler, T., Berdel, W.E., Kaufmann, M., Baldus, C.D., Schäfer-Eckart, K., Stuhlmann, R., Einsele, H., Krause, S.W., Serve, H., Hänel, M., Herbst, R., Neubauer, A., Sohlbach, K., Mayer, J., Middeke, J.M., Platzbecker, U., Schaich, M., Krämer, A., Röllig, C., Schetelig, J., Bornhäuser, M. and Ehninger, G. (2016) ‘Karyotype complexity and prognosis in acute myeloid leukemia’, Blood Cancer Journal, 6(1), p. e386. doi: 10.1038/bcj.2015.114.
  47. The cancer genome Atlas home page (2015) Available at: http://cancergenome.nih.gov/ (Accessed: 6 March 2016).
  48. Varn, F.S., Andrews, E.H. and Cheng, C. (2015) ‘Systematic analysis of hematopoietic gene expression profiles for prognostic prediction in acute myeloid leukemia’, Scientific Reports, 5, p. 16987. doi: 10.1038/srep16987.
  49. Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A. and Kinzler, K.W. (2013) ‘Cancer genome landscapes’, Science, 339(6127), pp. 1546–1558. doi: 10.1126/science.1235122.
  50. Wagner, H. (2007) ‘When silence isn’t golden’, Frontiers pp. 13–16.
  51. Ward, D.E. (2007) ‘Hitting the mark’, Frontiers pp. 12–17.
  52. Ward, D.E. (2008) ‘Wilder Genes’, Frontiers pp. 16–21.
  53. Ward, D.E. (2009) ‘Playing by miR’, Frontiers pp. 18–23.
  54. Ward, D.E. (2014) ‘A Wound That Never Heals’, Frontiers pp. 14–19.
  55. Ward, D.E. (2015) ‘How Genes Express Themselves… or Not’, Introduction to the Science of Cancer.  Coursera. The Ohio State University. Course materials. pp. 2–5. https://www.coursera.org/course/cancer
  56. Welch, J.S., Ley, T.J., Link, D.C., Miller, C.A., Larson, D.E., Koboldt, D.C., Wartman, L.D., Lamprecht, T.L., Liu, F., Xia, J., Kandoth, C., Fulton, R.S., McLellan, M.D., Dooling, D.J., Wallis, J.W., Chen, K., Harris, C.C., Schmidt, H.K., Kalicki-Veizer, J.M., Lu, C., Zhang, Q., Lin, L., O’Laughlin, M.D., McMichael, J.F., Delehaunty, K.D., Fulton, L.A., Magrini, V.J., McGrath, S.D., Demeter, R.T., Vickery, T.L., Hundal, J., Cook, L.L., Swift, G.W., Reed, J.P., Alldredge, P.A., Wylie, T.N., Walker, J.R., Watson, M.A., Heath, S.E., Shannon, W.D., Varghese, N., Nagarajan, R., Payton, J.E., Baty, J.D., Kulkarni, S., Klco, J.M., Tomasson, M.H., Westervelt, P., Walter, M.J., Graubert, T.A., DiPersio, J.F., Ding, L., Mardis, E.R. and Wilson, R.K. (2012) ‘The origin and evolution of mutations in acute myeloid leukemia’, Cell, 150(2), pp. 264–278. doi: 10.1016/j.cell.2012.06.023.
  57. Yoshida, K., Sanada, M., Shiraishi, Y., Nowak, D., Nagata, Y., Yamamoto, R., Sato, Y., Sato-Otsubo, A., Kon, A., Nagasaki, M., Chalkidis, G., Suzuki, Y., Shiosaka, M., Kawahata, R., Yamaguchi, T., Otsu, M., Obara, N., Sakata-Yanagimoto, M., Ishiyama, K., Mori, H., Nolte, F., Hofmann, W.-K., Miyawaki, S., Sugano, S., Haferlach, C., Koeffler, H.P., Shih, L.-Y., Haferlach, T., Chiba, S., Nakauchi, H., Miyano, S. and Ogawa, S. (2011) ‘Frequent pathway mutations of splicing machinery in myelodysplasia’, Nature, 478(7367), pp. 64–69. doi: 10.1038/nature10496.
  58. Zhu, X., Liu, X., Cheng, Z., Zhu, J., Xu, L., Wang, F., Qi, W., Yan, J., Liu, N., Sun, Z., Liu, H., Peng, X., Hao, Y., Zheng, N. and Wu, Q. (2016) ‘Quantitative analysis of global Proteome and Lysine Acetylome reveal the differential impacts of VPA and SAHA on HL60 cells’, Scientific Reports, 6, p. 19926. doi: 10.1038/srep19926.