Researchers have released a dataset detailing the molecular makeup of tumor cells from more than 500 patients with acute myeloid leukemia (AML).
The team discovered mutations not previously observed in AML and found associations between mutations and responses to certain therapies.
For instance, AML cases with FLT3, NPM1, and DNMT3A mutations proved sensitive to the BTK inhibitor ibrutinib.
The researchers described their findings in Nature.
The team also made their dataset available via Vizome, an online data viewer. Other researchers can use Vizome to find out which targeted therapies might be most effective against specific subsets of AML cells.
“People can get online, search our database, and very quickly get answers to ‘Is this a good drug?’ or ‘Is there a patient population my drug can work in?’” said study author Brian Druker, MD, of Oregon Health & Science University (OHSU) in Portland, Oregon.
Newly identified mutations
For this study, part of the Beat AML initiative, Dr. Druker and his colleagues performed whole-exome and RNA sequencing on 672 samples from 562 AML patients.
The team identified mutations in 11 genes that were called in 1% or more of patients in this dataset but had not been observed in previous AML sequencing studies. The genes were:
- CUB and Sushi multiple domains 2 (CSMD2)
- NAC alpha domain containing (NACAD)
- Teneurin transmembrane protein 2 (TENM2)
- Aggrecan (ACAN)
- ADAM metallopeptidase with thrombospondin type 1 motif 7 (ADAMTS7)
- Immunoglobulin-like and fibronectin type III domain containing 1 (IGFN1)
- Neurobeachin-like 2 (NBEAL2)
- Poly(U) binding splicing factor 60 (PUF60)
- Zinc-finger protein 687 (ZNF687)
- Cadherin EGF LAG sevenpass G-type receptor 2 (CELSR2)
- Glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B).
The researchers also assessed how AML cells from 409 of the patient samples responded to each of 122 targeted therapies.
The team found that mutations in TP53, ASXL1, NRAS, and KRAS caused “a broad pattern of drug resistance.”
However, cases with TP53 mutations were sensitive to elesclomol (a drug that targets cancer cell metabolism), cases with ASXL1 mutations were sensitive to the HDAC inhibitor panobinostat, and cases with KRAS/NRAS mutations were sensitive to MAPK inhibitors (with NRAS-mutated cases demonstrating greater sensitivity).
The researchers also found that IDH2 mutations “conferred sensitivity to a broad spectrum of drugs,” but IDH1 mutations were associated with resistance to most drugs.
As previously mentioned, the researchers found a significant association between mutations in FLT3, NPM1, and DNMT3A and sensitivity to ibrutinib. However, the team found that cases with DNMT3A mutations alone or mutations in DNMT3A and FLT3 were not significantly different from cases with wild-type genes.
On the other hand, cases with FLT3-ITD alone or any combination with a mutation in NPM1 (including mutations in all three genes) were significantly more sensitive to ibrutinib than cases with wild-type genes.
Cases with FLT3-ITD and mutations in NPM1 were sensitive to another kinase inhibitor, entospletinib, as well.
The researchers also found that mutations in both BCOR and RUNX1 correlated with increased sensitivity to four JAK inhibitors—momelotinib, ruxolitinib, tofacitinib, and JAK inhibitor I.
However, cases with BCOR mutations alone or mutations in BCOR and DNMT3A or SRSF2 showed no difference in sensitivity to the JAK inhibitors from cases with wild-type genes.
“We’re just starting to scratch the surface of what we can do when we analyze the data,” Dr. Druker said. “The real power comes when you start to integrate all that data. You can analyze what drug worked and why it worked.”
In fact, the researchers are already developing and initiating clinical trials to test hypotheses generated by this study.
“You can start to sense some momentum building with new, better therapeutics for AML patients, and, hopefully, this dataset will help fuel that momentum even further,” said study author Jeff Tyner, PhD, of the OHSU School of Medicine.
“We want to parlay this information into clinical trials as much as we can, and we also want the broader community to use this dataset to accelerate their own work.”
Funding for the current study was provided by grants from The Leukemia & Lymphoma Society, the National Cancer Institute, the National Library of Medicine, and other groups.