22 October 2018

A massive dataset has been released which details the molecular make-up of tumour cells from more than 500 patients with acute myeloid leukaemia (AML).

The largest cancer dataset of its kind, published last week in journal Nature, also includes how hundreds of individual patients' cells responded to a broad panel of drugs in laboratory screens.

Over 3,000 people in the UK are diagnosed every year with AML, which has the poorest prognosis of any blood cancer. Developing effective targeted treatments for AML has proved challenging, partly because AML is not a single disease, but a disease of many types with different driver mutations.

Alongside the academic paper, researchers and clinicians can now use a new online data viewer to find out in minutes what kinds of targeted therapies could be most effective against specific subsets of AML cells.

Drs Brian Druker and Jeffrey Tyner from Oregon Health & Science University, USA, say that the dataset represents the initial findings from the BeatAML programme, which is now moving into a clinical trial.

Dr Druker's team, which includes collaborators at 11 academic medical centres and 11 pharmaceutical and biotechnology companies, collected and analysed 672 samples of cancer cells from 562 patients.

The team assessed how tumour cells from 409 of the samples responded to each of 122 different targeted therapies. They also sequenced the DNA of each sample's protein-coding genes, as well as generating profiles of gene activity.

Dr Druker said: "The real power comes when you start to integrate all that data. You can analyse what drug worked and why it worked."

As an example of the potential of this dataset, the team reports it has identified a set of three genetic mutations that could enable AML patients to be treated with ibrutinib. 

Source: Tyner, J.W., Tognon, C.E., Bottomly, D., Wilmot, B., Kurtz, S.E., Savage, S.L., Long, N., Schultz, A.R., Traer, E., Abel, M., Agarwal, A., Blucher, A., Borate, U., Bryant, J., Burke, R., Carlos, A., Carpenter, R., Carroll, J., Chang, B.H., Coblentz, C., d'Almeida, A., Cook, R., Danilov, A., Dao, K.T., Degnin, M., Devine, D., Dibb J., Edwards, D.K., Eide, C.A., English, I., Glover, J., Henson, R., Ho, H., Jemal, A., Johnson, K., Johnson, R., Junio, B., Kaempf, A., Leonard, J., Lin, C., Liu, S.Q., Lo, P., Loriaux, M.M., Luty, S., Macey, T., MacManiman, J., Martinez, J., Mori, M., Nelson, D., Nichols, C., Peters, J., Ramsdill, J., Rofelty, A., Schuff, R., Searles, R., Segerdell, E., Smith, R.L., Spurgeon, S.E., Sweeney, T., Thapa, A., Visser, C., Wagner, J., Watanabe-Smith, K., Werth, K., Wolf, J., White, L., Yates, A., Zhang, H., Cogle, C.R., Collins, R.H., Connolly, D.C., Deininger, M.W., Drusbosky, L., Hourigan, C.S., Jordan, C.T., Kropf, P., Lin, T.L., Martinez, M.E., Medeiros, B.C., Pallapati, R.R., Pollyea, D.A., Swords, R.T., Watts, J.M., Weir, S.J., Wiest, D.L., Winters, R.M., McWeeney, S.K., Druker, B.J. (2018) “Functional genomic landscape of acute myeloid leukemia”, Nature, available at doi: 10.1038/s41586‑018‑0623‑z


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