journal contribution
posted on 2023-03-31, 23:50 authored by Peng Jin, Qiqi Jin, Xiaoling Wang, Ming Zhao, Fangyi Dong, Ge Jiang, Zeyi Li, Jie Shen, Wei Zhang, Shishuang Wu, Ran Li, Yunxiang Zhang, Xiaoyang Li, Junmin Li Supplementary Figure from Large-Scale In Vitro and In Vivo CRISPR-Cas9 Knockout Screens Identify a 16-Gene Fitness Score for Improved Risk Assessment in Acute Myeloid Leukemia
Funding
National Natural Science Foundation of China
National Key Research and Development Program of China
Shanghai Science Technology and Innovation Action Plan-Key Program on Medical Innovation Research
History
ARTICLE ABSTRACT
The molecular complexity of acute myeloid leukemia (AML) presents a considerable challenge to implementation of clinical genetic testing for accurate risk stratification. Identification of better biomarkers therefore remains a high priority to enable improving established stratification and guiding risk-adapted therapy decisions.
We systematically integrated and analyzed the genome-wide CRISPR-Cas9 data from more than 1,000 in vitro and in vivo knockout screens to identify the AML-specific fitness genes. A prognostic fitness score was developed using the sparse regression analysis in a training cohort of 618 cases and validated in five publicly available independent cohorts (n = 1,570) and our RJAML cohort (n = 157) with matched RNA sequencing and targeted gene sequencing performed.
A total of 280 genes were identified as AML fitness genes and a 16-gene AML fitness (AFG16) score was further generated and displayed highly prognostic power in more than 2,300 patients with AML. The AFG16 score was able to distill downstream consequences of several genetic abnormalities and can substantially improve the European LeukemiaNet classification. The multi-omics data from the RJAML cohort further demonstrated its clinical applicability. Patients with high AFG16 scores had significantly poor response to induction chemotherapy. Ex vivo drug screening indicated that patients with high AFG16 scores were more sensitive to the cell-cycle inhibitors flavopiridol and SNS-032, and exhibited strongly activated cell-cycle signaling.
Our findings demonstrated the utility of the AFG16 score as a powerful tool for better risk stratification and selecting patients most likely to benefit from chemotherapy and alternative experimental therapies.
