Corrigendum: Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia.

[This corrects the article DOI: 10.3389/fonc.2022.968340.].

Correction: Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia.

[This corrects the article DOI: 10.1371/journal.pone.0247093.].

Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia.

Risk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as ASXL1, RUNX1 and TP53. The intention of the present work was to evaluate the prognostic performance of Stellae-123 in external cohorts using RNAseq technology. For this, we evaluated the signature in 3 different AML cohorts (2 adult and 1 pediatric). Our results indicate that the prognostic performance of the Stellae-123 signature is reproducible in the 3 cohorts of patients. Additionally, we evidenced that the signature was superior to the European LeukemiaNet 2017 and the pediatric clinical risk scores in the prediction of survival at most of the evaluated time points. Furthermore, integration with age substantially enhanced the accuracy of the model. In conclusion, Stellae-123 is a reproducible machine learning algorithm based on a gene expression signature with promising utility in the field of AML.

Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling.

Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using gene expression data. We used gene expression data from two publicly available cohorts in order to create and validate a random forest predictor of survival, which we named ST-123. The most important variables in the model were age and the expression of KDM5B and LAPTM4B, two genes previously associated with the biology and prognostication of myeloid neoplasms. This classifier achieved high concordance indexes in the training and validation sets (0.7228 and 0.6988, respectively), and predictions were particularly accurate in patients at the highest risk of death. Additionally, ST-123 provided significant prognostic improvements in patients with high-risk mutations. Our results indicate that survival of patients with AML can be predicted to a great extent by applying machine learning tools to transcriptomic data, and that such predictions are particularly precise among patients with high-risk mutations.

Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia.

BACKGROUND: FLT3 mutation is present in 25-30% of all acute myeloid leukemias (AML), and it is associated with adverse outcome. FLT3 inhibitors have shown improved survival results in AML both as upfront treatment and in relapsed/refractory disease. Curiously, a variable proportion of wild-type FLT3 patients also responded to these drugs. METHODS: We analyzed 6 different transcriptomic datasets of AML cases. Differential expression between mutated and wild-type FLT3 AMLs was performed with the Wilcoxon-rank sum test. Hierarchical clustering was used to identify FLT3-mutation like AMLs. Finally, enrichment in recurrent mutations was performed with the Fisher's test. RESULTS: A FLT3 mutation-like gene expression pattern was identified among wild-type FLT3 AMLs. This pattern was highly enriched in NPM1 and DNMT3A mutants, and particularly in combined NPM1/DNMT3A mutants. CONCLUSIONS: We identified a FLT3 mutation-like gene expression pattern in AML which was highly enriched in NPM1 and DNMT3A mutations. Future analysis about the predictive role of this biomarker among wild-type FLT3 patients treated with FLT3 inhibitors is envisaged.

Performance of prognostic scoring systems in elderly patients with acute myeloid leukaemia on intensive chemotherapy: A PETHEMA registry study.

Selection of elderly patients (aged ≥60 years) for intensive chemotherapy treatment of acute myeloblastic leukaemia (AML) remains challenging. Several cooperative groups such as Acute Leukaemia French Association (ALFA), Haematological Oncology Clinical Studies Group (HOCSG) and MD Anderson Cancer Center (MDACC) have developed predictive models to select those patients who can benefit from intensive chemotherapy. Our purpose is to validate and compare these three models in a cohort of patients treated in real-life setting. For this, a total of 1724 elderly AML patients and treated with intensive chemotherapy regimens were identified in the PETHEMA registry. Median age was 67.2 years (range, 60-84,9) and median overall survival [OS] 9 months (95 % confidence interval [CI], 8.2-9.7). Taking into account the ALFA group's model, patients likely to benefit from intensive chemotherapy had longer OS (14 months, 95 % CI 12.3-15.7) than those unlikely to benefit (5 months, 95 % CI 4.1-5.9; p < 0.001). Significant differences in OS were observed between patients with favourable risk (17 months, 95 % CI 13.2-20.7), intermediate risk (11 months, 95 % CI 9.3-12.6) and adverse risk (6 months, 95 % CI 5.1-6.4; p < 0.001) according to the HOCSG model. No significant differences in OS were observed between patients with 0, 1, 2 or ≥3 points according to the MDACC model. However, when patients with ≥1 point were compared with those with 0 points, median OS was significantly longer in the latter [15 months (95 % CI 12.1-17.8) vs 7 (95 % CI 5.7-8.5)]. This retrospective study validates predictive models proposed by the ALFA, HOCSG and MDACC groups in this real-life cohort.