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Diffuse Large B-cell Lymphoma (DLBCL) is the most common type of aggressive lymphoma. Approximately 60% of fit patients achieve curation with immunochemotherapy, but the remaining patients relapse or have refractory disease, which predicts a short survival. Traditionally, risk stratification in DLBCL has been based on scores that combine clinical variables. Other methodologies have been developed based on the identification of novel molecular features, such as mutational profiles and gene expression signatures. Recently, we developed the LymForest-25 profile, which provides a personalized survival risk prediction based on the integration of transcriptomic and clinical features using an artificial intelligence system. In the present report, we studied the relationship between the molecular variables included in LymForest-25 in the context of the data released by the REMoDL-B trial, which evaluated the addition of bortezomib to the standard treatment (R-CHOP) in the upfront setting of DLBCL. For this, we retrained the machine learning model of survival on the group of patients treated with R-CHOP (N=469) and then made survival predictions for those patients treated with bortezomib plus R-CHOP (N=459). According to these results, the RB-CHOP scheme achieved a 30% reduction in the risk of progression or death for the 50% of DLBCL patients at higher molecular risk (p-value 0.03), potentially expanding the effectiveness of this treatment to a wider patient population as compared with other previously defined risk groups., Competing Interests: AMO reports honoraria for lectures and participation in advisory boards from Janssen, Takeda, Abbey, Amgen, Novartis, Gilead and AstraZeneca; research grants from Roche, Pfizer and Celgene-BMS and funds for conference organization from Jassen, Takeda, Abbey, Amgen, Novartis, Gilead, Roche, Bristol-Myers-Squibb, Glaxo-Smith-Klyne, Incyte and Pfizer. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest., (Copyright © 2023 Mosquera Orgueira, Díaz Arías, Serrano Martín, Portela Piñeiro, Cid López, Peleteiro Raíndo, Bao Pérez, González Pérez, Pérez Encinas, Fraga Rodríguez, Vallejo Llamas and Bello López.)

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., Competing Interests: AO has received honoraria for lectures and participation in advisory boards from Janssen and AstraZeneca. AO has received research grants from Roche and Celgene-BMS. JL has received honoraria for lectures and participation in advisory boards from Janssen, Abbey and Roche. JL has received research funds from Roche and Celgene-BMS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer GS declared a shared affiliation with one of the authors CC, to the handling editor at time of review., (Copyright © 2022 Mosquera Orgueira, Cid López, Peleteiro Raíndo, Abuín Blanco, Díaz Arias, González Pérez, Antelo Rodríguez, Bao Pérez, Ferreiro Ferro, Aliste Santos, Pérez Encinas, Fraga Rodríguez, Cerchione, Mozas and Bello López.)

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., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Introduction: Acute myeloblastic leukemia (AML) is the most frequent type of acute leukemia in adults with an incidence of 4.2 cases per 100,000 inhabitants and poor 5-year survival. Patients with mutations in the FMS-like tyrosine kinase 3 ( FLT3) gene have poor survival and higher relapse rates compared with wild-type cases. Areas covered: Several FLT3 inhibitors have been proved in FLT3 mut AML patients, with differences in their pharmacokinetics, kinase inhibitory and adverse events profiles. First-generation multi-kinase inhibitors (midostaurin, sorafenib, lestaurtinib) target multiple proteins, whereassecond-generation inhibitors (crenolanib, quizartinib, gilteritinib) are more specific and potent inhibitors of FLT3, so they are associated with less off-target toxic effects. All of these drugs have primary and acquired mechanisms of resistance, and therefore their combinations with other drugs (checkpoint inhibitors, hypomethylating agents, standard chemotherapy) and its application in different clinical settings are under study. Expert opinion: The recent clinical development of various FLT3 inhibitors for the treatment of FLT3 mut AML is an effective therapeutic strategy. However, there are unique toxicities and drug-drug interactions that need to be resolved. It is necessary to understand the mechanisms of toxicity in order to recognize and manage them adequately.

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., Competing Interests: The authors declare no conflicts of interest.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mosquera Orgueira, Peleteiro Raíndo, Cid López, Díaz Arias, González Pérez, Antelo Rodríguez, Alonso Vence, Bao Pérez, Ferreiro Ferro, Albors Ferreiro, Abuín Blanco, Fontanes Trabazo, Cerchione, Martinnelli, Montesinos Fernández, Mateo Pérez Encinas and Luis Bello López.)

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.

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.

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.