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. 2019 Oct 1;9(10):79.
doi: 10.1038/s41408-019-0244-2.

A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1

Nikhil Patkar  1 , Anam Fatima Shaikh  2 , Chinmayee Kakirde  2 , Shrinidhi Nathany  2 , Hridya Ramesh  2 , Prasanna Bhanshe  2 , Swapnali Joshi  2 , Shruti Chaudhary  2 , Sadhana Kannan  3 , Syed Hasan Khizer  4 , Gaurav Chatterjee  2 , Prashant Tembhare  2 , Dhanalaxmi Shetty  5 , Anant Gokarn  4 , Sachin Punatkar  4 , Avinash Bonda  4 , Lingaraj Nayak  4 , Hasmukh Jain  4 , Navin Khattry  4 , Bhausaheb Bagal  4 , Manju Sengar  4 , Sumeet Gujral  2 , Papagudi Subramanian  2
Affiliations

A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1

Nikhil Patkar et al. Blood Cancer J. .
No abstract available

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1. A total of 389 somatic mutations were harbored among 110 patients.
The above circos plot (1 A) highlights the genomic complexity of NPM1-mutated AML. Commonly occurring gene mutations are colored. The scoring system is detailed in panel b. Clinical relevance of the machine-learning-derived genetic score for NPM1-mutated AML is also depicted here. The Kaplan–Meier plot in the top-right section (c) shows the clinical relevance of genetic risk when factored for overall survival (OS) and plot on the lower right (d) for relapse-free survival (RFS)
See this image and copyright information in PMC

References

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