A Two-Gene Classifier for Predicting Response to the Farnesyltransferase Inhibitor Tipifarnib in Acute Myeloid Leukemia.

Raponi, Mitch; Lancet, Jeffrey E.; Fan, Huiying; Dossey, Lesley; Lee, Grace; Gojo, Ivana; Feldman, Eric J.; Gotlib, Jason; Morris, Lawrence E.; Greenberg, Peter L.; Wright, John J.; Harousseau, Jean‐Luc; Löwenberg, Bob; Stone, Richard; Porre, Peter De; Wang, Yixin; Karp, Judith E.

doi:10.1182/blood.v110.11.1445.1445

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“…All GEO databases in this article can be downloaded on the GEO website (https://www.ncbi.nlm.nih.gov/geo/) [24][25][26][27][28][29][30]. The TCGA dataset comes from http://www.tcga.org/ [31].…”

Section: Supplementary Informationmentioning

confidence: 99%

The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia

Zeng

Cui

Huang

et al. 2021

BMC Med

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Background The high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest. Methods The patients’ gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy. Results After analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P< 0.001). Besides, the model can perform well in multiple datasets and could further stratify the cytogenetically normal AML patients and intermediate-risk AML population. Compared with the European Leukemia Net Risk Stratification System and other AML prognostic models, our model had the highest C-index and the largest AUC of the ROC curve, which demonstrated that our model had the best prognostic efficacy. Conclusion A prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.

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Section: Supplementary Informationmentioning

confidence: 99%