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

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

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.

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.

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools.

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

Simple Summary

The global importance of rare variants in tumorigenesis has been addressed by some pan-cancer analysis, revealing significant enrichments in protein-truncating variants affecting genes such as ATM, BRCA1/2, BRIP1, and MSH6. Germline variants can influence treatment response and contribute to the development of treatment-related second neoplasms, especially in childhood leukemia. We aimed to analyze the genomes of patients with B-cell lymphoproliferative disorders for the discovery of genes enriched in rare pathogenic variants. We discovered a significant enrichment for two genes in germline rare and dysfunctional variants. Additionally, we detected rare and likely pathogenic variants associated with disease prognosis and potential druggability, indicating a relevant role of these events in the variability of cancer phenotypes.

Abstract

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.

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

BACKGROUND

Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data.

METHODS

Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set.

RESULTS

Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942).

CONCLUSION

Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

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.

New Recurrent Structural Aberrations in the Genome of Chronic Lymphocytic Leukemia Based on Exome-Sequencing Data

Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in Western countries, and it is characterized by recurrent large genomic rearrangements. During the last decades, array techniques have expanded our knowledge about CLL's karyotypic aberrations. The advent of large sequencing databases expanded our knowledge cancer genomics to an unprecedented resolution and enabled the detection of small-scale structural aberrations in the cancer genome. In this study, we have performed exome-sequencing-based copy number aberration (CNA) and loss of heterozygosity (LOH) analysis in order to detect new recurrent structural aberrations. We describe 54 recurrent focal CNAs enriched in cancer-related pathways, and their association with gene expression and clinical evolution. Furthermore, we discovered recurrent large copy number neutral LOH events affecting key driver genes, and we recapitulate most of the large CNAs that characterize the CLL genome. These results provide "proof-of-concept" evidence supporting the existence of new genes involved in the pathogenesis of CLL.

The association of germline variants with chronic lymphocytic leukemia outcome suggests the implication of novel genes and pathways in clinical evolution

BACKGROUND

Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disorder in western countries and is characterized by a remarkable clinical heterogeneity. During the last decade, multiple genomic studies have identified a myriad of somatic events driving CLL proliferation and aggressivity. Nevertheless, and despite the mounting evidence of inherited risk for CLL development, the existence of germline variants associated with clinical outcomes has not been addressed in depth.

METHODS

Exome sequencing data from control leukocytes of CLL patients involved in the International Cancer Genome Consortium (ICGC) was used for genotyping. Cox regression was used to detect variants associated with clinical outcomes. Gene and pathways level associations were also calculated.

RESULTS

Single nucleotide polymorphisms in PPP4R2 and MAP3K4 were associated with earlier treatment need. A gene-level analysis evidenced a significant association of RIPK3 with both treatment need and survival. Furthermore, germline variability in pathways such as apoptosis, cell-cycle, pentose phosphate, GNα13 and Nitric oxide was associated with overall survival.

CONCLUSION

Our results support the existence of inherited conditionants of CLL evolution and points towards genes and pathways that may results useful as biomarkers of disease outcome. More research is needed to validate these findings.

Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns

Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in western countries. CLL evolution is frequently indolent, and treatment is mostly reserved for those patients with signs or symptoms of disease progression. In this work, we used RNA sequencing data from the International Cancer Genome Consortium CLL cohort to determine new gene expression patterns that correlate with clinical evolution.We determined that a 290-gene expression signature, in addition to immunoglobulin heavy chain variable region (IGHV) mutation status, stratifies patients into four groups with notably different time to first treatment. This finding was confirmed in an independent cohort. Similarly, we present a machine learning algorithm that predicts the need for treatment within the first 5 years following diagnosis using expression data from 2,198 genes. This predictor achieved 90% precision and 89% accuracy when classifying independent CLL cases. Our findings indicate that CLL progression risk largely correlates with particular transcriptomic patterns and paves the way for the identification of high-risk patients who might benefit from prompt therapy following diagnosis.

Magnetic control of dipolaritons in quantum dots

Dipolaritons are quasiparticles that arise in coupled quantum wells embedded in a microcavity, they are a superposition of a photon, a direct exciton and an indirect exciton. We propose the existence of dipolaritons in a system of two coupled quantum dots inside a microcavity in direct analogy with the quantum well case and find that, despite some similarities, dipolaritons in quantum dots have different properties and can lead to true dark polariton states. We use a finite system theory to study the effects of the magnetic field on the system, including the emission, and find that it can be used as a control parameter of the properties of excitons and dipolaritons, and the overall magnetic behaviour of the structure.

Determinants of differential migration of mouse spleen cells treated with concanavalin A

The effects of Con A on homing patterns of mouse spleen cells were investigated. After 1 to 18 hr incubation with or without the mitogen, cells were labeled with 51Cr and injected i.v. into syngeneic hosts. Treatment of spleen cells with Con A decreased localization in lymph nodes 75 to 95% and inhibited homing to recipient spleens 17 to 50%. Conversely, localization in liver and lungs was increased. Migration of cells labeled with both 125I-Con A and 51Cr revealed that cells binding the largest amount of Con A were trapped in lungs whereas cells migrating to lymph nodes and spleen had a much lower Con A content. Elution of Con A from lymphocyte surfaces with methyl-alpha-D-mannoside (MAM) reduced the percentage of cells localizing in lungs, with a concomitant increase in the percentage of cells migrating to lymph nodes. However, even after MAM treatment, a cell population with a relatively large Con A content localized in the lungs. These data indicate that both surface-bound Con A and Con A-induced changes in lymphocyte membranes inhibit migration of Con A-activated cells to recipient lymph nodes. They suggest that after Con A treatment lymph node-seeking cells do not migrate normally because of preferential sequestration in lungs of lymphocytes with either Con A-binding sites of increased density or avidity or with an increased propensity for Con A internalization.