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

Abstract

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 over 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-seq 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 AML patients. The AFG16 score was able to distil 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.

Identification of IKZF1 genetic mutations as new molecular subtypes in acute myeloid leukaemia

“…Primary blasts were obtained according to the Declaration of Helsinki at disease onset from bone marrow of 475 AML patients with least 20% abnormal bone marrow blasts. RNA sequencing and targeted screening of 100 common leukaemia‐related genes was performed in all primary AML patients from Ruijin Hospital as previously described 28 . Clinical information of 23 AML with IKZF1 mutations is provided (Table S1).…”

“…RNA sequencing and targeted screening of 100 common leukaemia‐related genes was performed in all primary AML patients from Ruijin Hospital as previously described. 28 Clinical information of 23 AML with IKZF1 mutations is provided (Table S1 ). Informed consent was obtained according to procedures approved by the Institutional Review Board from Ruijin Hospital, affiliated to Shanghai Jiao Tong University School of Medicine.…”

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“…To further ascertain the strength and robustness of our model, we compared the NADM8 score with previously reported transcriptomebased prognostic models, such as the 17-gene stemness score (LSC17) (8) and the 16-gene AML fitness (AFG16) (9). When adjusting for common clinical covariates, the NADM8 model exhibited generally superior performance in multiple datasets (TCGA, BeatAML, and HOVON cohorts), as reflected in the most significant P value in all multivariate Cox models, whereas other models only retained significant prognostic value in a subset of these cohorts.…”

“…With the widespread adoption of next-generation sequencing (NGS) technologies, it becomes feasible to comprehensively explore the genomic and transcriptomic profiles, which may largely promote the precise classification and prognostic evaluation of AML patients (5)(6)(7). To date, numerous genome-and transcriptome-based AML prognostic models have been developed, including the 17-gene stemness score (LSC17) defined by stem cell subsets (8), the 16gene AML fitness (AFG16) from large-scale CRISPR-Cas9 screening (9), and the GENE4 generated by capturing intratumor heterogeneity of AML (10), indicating that the gene transcriptional data could capture the heterogeneity of AML patients and largely refine the traditional risk assignment system. In this context, integrating multiomics data offers insights to identify novel molecular markers with prognostic and therapeutic value in AML, enabling more precise therapy and refined stratification, which represents a major area of future research.…”

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“…With the widespread adoption of next-generation sequencing (NGS) technologies, it becomes feasible to comprehensively explore the genomic and transcriptomic profiles, which may largely promote the precise classification and prognostic evaluation of AML patients (5)(6)(7). To date, numerous genome-and transcriptome-based AML prognostic models have been developed, including the 17-gene stemness score (LSC17) defined by stem cell subsets (8), the 16gene AML fitness (AFG16) from large-scale CRISPR-Cas9 screening (9), and the GENE4 generated by capturing intratumor heterogeneity of AML (10), indicating that the gene transcriptional data could capture the heterogeneity of AML patients and largely refine the traditional risk assignment system. In this context, integrating multiomics data offers insights to identify novel molecular markers with prognostic and therapeutic value in AML, enabling more precise therapy and refined stratification, which represents a major area of future research.…”