4.7 Article

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

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CLINICAL CANCER RESEARCH
卷 28, 期 18, 页码 4033-4044

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AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-22-1618

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  1. National Natural Science Foundation of China [81870110]
  2. National Key Research and Development Program of China [YFA0905900]
  3. Shanghai Science Technology and Innovation Action Plan-Key Program on Medical Innovation Research [21Y31920400]
  4. Youth Development Program of Ruijin Hospital [KY00305]
  5. Center for High Performance Computing at Shanghai Jiao Tong University

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In this study, the researchers identified AML-specific fitness genes and developed a prognostic fitness score using CRISPR-Cas9 data. The score showed high prognostic power and improved risk stratification in AML patients. It also helped guide treatment decisions by identifying patients most likely to benefit from chemotherapy and experimental therapies.
Purpose: 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. Experimental Design: 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. Results: 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. Conclusions: 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.

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