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.].

Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes.

B-cell lymphoproliferative disorders exhibit a diverse spectrum of diagnostic entities with heterogeneous behaviour. Multiple efforts have focused on the determination of the genomic drivers of B-cell lymphoma subtypes. In the meantime, the aggregation of diverse tumors in pan-cancer genomic studies has become a useful tool to detect new driver genes, while enabling the comparison of mutational patterns across tumors. Here we present an integrated analysis of 354 B-cell lymphoid disorders. 112 recurrently mutated genes were discovered, of which KMT2D, CREBBP, IGLL5 and BCL2 were the most frequent, and 31 genes were putative new drivers. Mutations in CREBBP, TNFRSF14 and KMT2D predominated in follicular lymphoma, whereas those in BTG2, HTA-A and PIM1 were more frequent in diffuse large B-cell lymphoma. Additionally, we discovered 31 significantly mutated protein networks, reinforcing the role of genes such as CREBBP, EEF1A1, STAT6, GNA13 and TP53, but also pointing towards a myriad of infrequent players in lymphomagenesis. Finally, we report aberrant expression of oncogenes and tumor suppressors associated with novel noncoding mutations (DTX1 and S1PR2), and new recurrent copy number aberrations affecting immune check-point regulators (CD83, PVR) and B-cell specific genes (TNFRSF13C). Our analysis expands the number of mutational drivers of B-cell lymphoid neoplasms, and identifies several differential somatic events between disease subtypes.

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.

Detection of Rare Germline Variants in the Genomes of Patients with B-Cell Neoplasms.

There is growing evidence indicating the implication of germline variation in cancer predisposition and prognostication. Here, we describe an analysis of likely disruptive rare variants across the genomes of 726 patients with B-cell lymphoid neoplasms. We discovered a significant enrichment for two genes in rare dysfunctional variants, both of which participate in the regulation of oxidative stress pathways (CHMP6 and GSTA4). Additionally, we detected 1675 likely disrupting variants in genes associated with cancer, of which 44.75% were novel events and 7.88% were protein-truncating variants. Among these, the most frequently affected genes were ATM, BIRC6, CLTCL1A, and TSC2. Homozygous or germline double-hit variants were detected in 28 cases, and coexisting somatic events were observed in 17 patients, some of which affected key lymphoma drivers such as ATM, KMT2D, and MYC. Finally, we observed that variants in six different genes were independently associated with shorter survival in CLL. Our study results support an important role for rare germline variation in the pathogenesis and prognosis of B-cell lymphoid neoplasms.

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.

Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models.

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.

FLT3 inhibitors in the treatment of acute myeloid leukemia: current status and future perspectives.

Mutations in the FMS-like tyrosine kinase 3 (FLT3) gene arise in 25-30% of all acute myeloid leukemia (AML) patients. These mutations lead to constitutive activation of the protein product and are divided in two broad types: internal tandem duplication (ITD) of the juxtamembrane domain (25% of cases) and point mutations in the tyrosine kinase domain (TKD). Patients with FLT3 ITD mutations have a high relapse risk and inferior cure rates, whereas the role of FLT3 TKD mutations still remains to be clarified. Additionally, growing research indicates that FLT3 status evolves through a disease continuum (clonal evolution), where AML cases can acquire FLT3 mutations at relapse - not present in the moment of diagnosis. Several FLT3 inhibitors have been tested in patients with FLT3-mutated AML. These drugs exhibit different kinase inhibitory profiles, pharmacokinetics and adverse events. First-generation multi-kinase inhibitors (sorafenib, midostaurin, lestaurtinib) are characterized by a broad-spectrum of drug targets, whereas second-generation inhibitors (quizartinib, crenolanib, gilteritinib) show more potent and specific FLT3 inhibition, and are thereby accompanied by less toxic effects. Notwithstanding, all FLT3 inhibitors face primary and acquired mechanisms of resistance, and therefore the combinations with other drugs (standard chemotherapy, hypomethylating agents, checkpoint inhibitors) and its application in different clinical settings (upfront therapy, maintenance, relapsed or refractory disease) are under study in a myriad of clinical trials. This review focuses on the role of FLT3 mutations in AML, pharmacological features of FLT3 inhibitors, known mechanisms of drug resistance and accumulated evidence for the use of FLT3 inhibitors in different clinical settings.